test_engine.py 167 KB
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# coding: utf-8
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import copy
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import itertools
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import json
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import math
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import pickle
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import platform
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import random
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import re
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from os import getenv
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from pathlib import Path
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from shutil import copyfile
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import numpy as np
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import psutil
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import pytest
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from scipy.sparse import csr_matrix, isspmatrix_csc, isspmatrix_csr
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from sklearn.datasets import load_svmlight_file, make_blobs, make_multilabel_classification
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from sklearn.metrics import average_precision_score, log_loss, mean_absolute_error, mean_squared_error, roc_auc_score
from sklearn.model_selection import GroupKFold, TimeSeriesSplit, train_test_split
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import lightgbm as lgb
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from lightgbm.compat import PANDAS_INSTALLED, pd_DataFrame, pd_Series
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from .utils import (
    SERIALIZERS,
    dummy_obj,
    load_breast_cancer,
    load_digits,
    load_iris,
    logistic_sigmoid,
    make_synthetic_regression,
    mse_obj,
    pickle_and_unpickle_object,
    sklearn_multiclass_custom_objective,
    softmax,
)
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decreasing_generator = itertools.count(0, -1)


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def logloss_obj(preds, train_data):
    y_true = train_data.get_label()
    y_pred = logistic_sigmoid(preds)
    grad = y_pred - y_true
    hess = y_pred * (1.0 - y_pred)
    return grad, hess
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def multi_logloss(y_true, y_pred):
    return np.mean([-math.log(y_pred[i][y]) for i, y in enumerate(y_true)])

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def top_k_error(y_true, y_pred, k):
    if k == y_pred.shape[1]:
        return 0
    max_rest = np.max(-np.partition(-y_pred, k)[:, k:], axis=1)
    return 1 - np.mean((y_pred[np.arange(len(y_true)), y_true] > max_rest))


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def constant_metric(preds, train_data):
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    return ("error", 0.0, False)
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def decreasing_metric(preds, train_data):
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    return ("decreasing_metric", next(decreasing_generator), False)
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def categorize(continuous_x):
    return np.digitize(continuous_x, bins=np.arange(0, 1, 0.01))


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def test_binary():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "metric": "binary_logloss",
        "verbose": -1,
        "num_iteration": 50,  # test num_iteration in dict here
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    }
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = log_loss(y_test, gbm.predict(X_test))
    assert ret < 0.14
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    assert len(evals_result["valid_0"]["binary_logloss"]) == 50
    assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret)
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def test_rf():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "boosting_type": "rf",
        "objective": "binary",
        "bagging_freq": 1,
        "bagging_fraction": 0.5,
        "feature_fraction": 0.5,
        "num_leaves": 50,
        "metric": "binary_logloss",
        "verbose": -1,
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    }
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = log_loss(y_test, gbm.predict(X_test))
    assert ret < 0.19
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    assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret)
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@pytest.mark.parametrize("objective", ["regression", "regression_l1", "huber", "fair", "poisson", "quantile"])
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def test_regression(objective):
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    X, y = make_synthetic_regression()
    y = np.abs(y)
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"objective": objective, "metric": "l2", "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = mean_squared_error(y_test, gbm.predict(X_test))
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    if objective == "huber":
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        assert ret < 430
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    elif objective == "fair":
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        assert ret < 296
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    elif objective == "poisson":
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        assert ret < 193
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    elif objective == "quantile":
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        assert ret < 1311
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    else:
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        assert ret < 343
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    assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret)
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def test_missing_value_handle():
    X_train = np.zeros((100, 1))
    y_train = np.zeros(100)
    trues = random.sample(range(100), 20)
    for idx in trues:
        X_train[idx, 0] = np.nan
        y_train[idx] = 1
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

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    params = {"metric": "l2", "verbose": -1, "boost_from_average": False}
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    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = mean_squared_error(y_train, gbm.predict(X_train))
    assert ret < 0.005
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    assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret)
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def test_missing_value_handle_more_na():
    X_train = np.ones((100, 1))
    y_train = np.ones(100)
    trues = random.sample(range(100), 80)
    for idx in trues:
        X_train[idx, 0] = np.nan
        y_train[idx] = 0
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

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    params = {"metric": "l2", "verbose": -1, "boost_from_average": False}
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    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = mean_squared_error(y_train, gbm.predict(X_train))
    assert ret < 0.005
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    assert evals_result["valid_0"]["l2"][-1] == pytest.approx(ret)
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def test_missing_value_handle_na():
    x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan]
    y = [1, 1, 1, 1, 0, 0, 0, 0, 1]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "zero_as_missing": False,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    np.testing.assert_allclose(pred, y)
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.999
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_missing_value_handle_zero():
    x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan]
    y = [0, 1, 1, 1, 0, 0, 0, 0, 0]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "zero_as_missing": True,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    np.testing.assert_allclose(pred, y)
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.999
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_missing_value_handle_none():
    x = [0, 1, 2, 3, 4, 5, 6, 7, np.nan]
    y = [0, 1, 1, 1, 0, 0, 0, 0, 0]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "use_missing": False,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    assert pred[0] == pytest.approx(pred[1])
    assert pred[-1] == pytest.approx(pred[0])
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.83
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_categorical_handle():
    x = [0, 1, 2, 3, 4, 5, 6, 7]
    y = [0, 1, 0, 1, 0, 1, 0, 1]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "min_data_per_group": 1,
        "cat_smooth": 1,
        "cat_l2": 0,
        "max_cat_to_onehot": 1,
        "zero_as_missing": True,
        "categorical_column": 0,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    np.testing.assert_allclose(pred, y)
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.999
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_categorical_handle_na():
    x = [0, np.nan, 0, np.nan, 0, np.nan]
    y = [0, 1, 0, 1, 0, 1]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "min_data_per_group": 1,
        "cat_smooth": 1,
        "cat_l2": 0,
        "max_cat_to_onehot": 1,
        "zero_as_missing": False,
        "categorical_column": 0,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    np.testing.assert_allclose(pred, y)
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.999
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_categorical_non_zero_inputs():
    x = [1, 1, 1, 1, 1, 1, 2, 2]
    y = [1, 1, 1, 1, 1, 1, 0, 0]

    X_train = np.array(x).reshape(len(x), 1)
    y_train = np.array(y)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_train, y_train)

    params = {
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        "objective": "regression",
        "metric": "auc",
        "verbose": -1,
        "boost_from_average": False,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "min_data_per_group": 1,
        "cat_smooth": 1,
        "cat_l2": 0,
        "max_cat_to_onehot": 1,
        "zero_as_missing": False,
        "categorical_column": 0,
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    }
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    pred = gbm.predict(X_train)
    np.testing.assert_allclose(pred, y)
    ret = roc_auc_score(y_train, pred)
    assert ret > 0.999
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    assert evals_result["valid_0"]["auc"][-1] == pytest.approx(ret)
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def test_multiclass():
    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 10, "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, params=params)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params)
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = multi_logloss(y_test, gbm.predict(X_test))
    assert ret < 0.16
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    assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret)
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def test_multiclass_rf():
    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "boosting_type": "rf",
        "objective": "multiclass",
        "metric": "multi_logloss",
        "bagging_freq": 1,
        "bagging_fraction": 0.6,
        "feature_fraction": 0.6,
        "num_class": 10,
        "num_leaves": 50,
        "min_data": 1,
        "verbose": -1,
        "gpu_use_dp": True,
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    }
    lgb_train = lgb.Dataset(X_train, y_train, params=params)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params)
    evals_result = {}
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    gbm = lgb.train(
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        params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
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    )
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    ret = multi_logloss(y_test, gbm.predict(X_test))
    assert ret < 0.23
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    assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret)
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def test_multiclass_prediction_early_stopping():
    X, y = load_digits(n_class=10, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 10, "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, params=params)
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    gbm = lgb.train(params, lgb_train, num_boost_round=50)
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    pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
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    ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter))
    assert ret < 0.8
    assert ret > 0.6  # loss will be higher than when evaluating the full model

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    pred_parameter["pred_early_stop_margin"] = 5.5
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    ret = multi_logloss(y_test, gbm.predict(X_test, **pred_parameter))
    assert ret < 0.2


def test_multi_class_error():
    X, y = load_digits(n_class=10, return_X_y=True)
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    params = {"objective": "multiclass", "num_classes": 10, "metric": "multi_error", "num_leaves": 4, "verbose": -1}
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    lgb_data = lgb.Dataset(X, label=y)
    est = lgb.train(params, lgb_data, num_boost_round=10)
    predict_default = est.predict(X)
    results = {}
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    est = lgb.train(
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        dict(params, multi_error_top_k=1),
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        lgb_data,
        num_boost_round=10,
        valid_sets=[lgb_data],
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        callbacks=[lgb.record_evaluation(results)],
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    )
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    predict_1 = est.predict(X)
    # check that default gives same result as k = 1
    np.testing.assert_allclose(predict_1, predict_default)
    # check against independent calculation for k = 1
    err = top_k_error(y, predict_1, 1)
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    assert results["training"]["multi_error"][-1] == pytest.approx(err)
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    # check against independent calculation for k = 2
    results = {}
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    est = lgb.train(
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        dict(params, multi_error_top_k=2),
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        lgb_data,
        num_boost_round=10,
        valid_sets=[lgb_data],
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        callbacks=[lgb.record_evaluation(results)],
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    )
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    predict_2 = est.predict(X)
    err = top_k_error(y, predict_2, 2)
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    assert results["training"]["multi_error@2"][-1] == pytest.approx(err)
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    # check against independent calculation for k = 10
    results = {}
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    est = lgb.train(
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        dict(params, multi_error_top_k=10),
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        lgb_data,
        num_boost_round=10,
        valid_sets=[lgb_data],
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        callbacks=[lgb.record_evaluation(results)],
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    )
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    predict_3 = est.predict(X)
    err = top_k_error(y, predict_3, 10)
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    assert results["training"]["multi_error@10"][-1] == pytest.approx(err)
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    # check cases where predictions are equal
    X = np.array([[0, 0], [0, 0]])
    y = np.array([0, 1])
    lgb_data = lgb.Dataset(X, label=y)
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    params["num_classes"] = 2
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    results = {}
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    lgb.train(params, lgb_data, num_boost_round=10, valid_sets=[lgb_data], callbacks=[lgb.record_evaluation(results)])
    assert results["training"]["multi_error"][-1] == pytest.approx(1)
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    results = {}
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    lgb.train(
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        dict(params, multi_error_top_k=2),
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        lgb_data,
        num_boost_round=10,
        valid_sets=[lgb_data],
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        callbacks=[lgb.record_evaluation(results)],
504
    )
505
    assert results["training"]["multi_error@2"][-1] == pytest.approx(0)
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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
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def test_auc_mu():
    # should give same result as binary auc for 2 classes
    X, y = load_digits(n_class=10, return_X_y=True)
    y_new = np.zeros((len(y)))
    y_new[y != 0] = 1
    lgb_X = lgb.Dataset(X, label=y_new)
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    params = {"objective": "multiclass", "metric": "auc_mu", "verbose": -1, "num_classes": 2, "seed": 0}
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    results_auc_mu = {}
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    lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc_mu)])
    params = {"objective": "binary", "metric": "auc", "verbose": -1, "seed": 0}
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    results_auc = {}
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    lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc)])
    np.testing.assert_allclose(results_auc_mu["training"]["auc_mu"], results_auc["training"]["auc"])
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    # test the case where all predictions are equal
    lgb_X = lgb.Dataset(X[:10], label=y_new[:10])
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    params = {
        "objective": "multiclass",
        "metric": "auc_mu",
        "verbose": -1,
        "num_classes": 2,
        "min_data_in_leaf": 20,
        "seed": 0,
    }
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    results_auc_mu = {}
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    lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_auc_mu)])
    assert results_auc_mu["training"]["auc_mu"][-1] == pytest.approx(0.5)
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    # test that weighted data gives different auc_mu
    lgb_X = lgb.Dataset(X, label=y)
    lgb_X_weighted = lgb.Dataset(X, label=y, weight=np.abs(np.random.normal(size=y.shape)))
    results_unweighted = {}
    results_weighted = {}
    params = dict(params, num_classes=10, num_leaves=5)
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    lgb.train(
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        params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_unweighted)]
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    )
    lgb.train(
        params,
        lgb_X_weighted,
        num_boost_round=10,
        valid_sets=[lgb_X_weighted],
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        callbacks=[lgb.record_evaluation(results_weighted)],
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    )
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    assert results_weighted["training"]["auc_mu"][-1] < 1
    assert results_unweighted["training"]["auc_mu"][-1] != results_weighted["training"]["auc_mu"][-1]
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    # test that equal data weights give same auc_mu as unweighted data
    lgb_X_weighted = lgb.Dataset(X, label=y, weight=np.ones(y.shape) * 0.5)
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    lgb.train(
        params,
        lgb_X_weighted,
        num_boost_round=10,
        valid_sets=[lgb_X_weighted],
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        callbacks=[lgb.record_evaluation(results_weighted)],
    )
    assert results_unweighted["training"]["auc_mu"][-1] == pytest.approx(
        results_weighted["training"]["auc_mu"][-1], abs=1e-5
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    )
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    # should give 1 when accuracy = 1
    X = X[:10, :]
    y = y[:10]
    lgb_X = lgb.Dataset(X, label=y)
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    params = {"objective": "multiclass", "metric": "auc_mu", "num_classes": 10, "min_data_in_leaf": 1, "verbose": -1}
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    results = {}
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    lgb.train(params, lgb_X, num_boost_round=100, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results)])
    assert results["training"]["auc_mu"][-1] == pytest.approx(1)
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    # test loading class weights
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    Xy = np.loadtxt(
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        str(Path(__file__).absolute().parents[2] / "examples" / "multiclass_classification" / "multiclass.train")
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    )
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    y = Xy[:, 0]
    X = Xy[:, 1:]
    lgb_X = lgb.Dataset(X, label=y)
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    params = {
        "objective": "multiclass",
        "metric": "auc_mu",
        "auc_mu_weights": [0, 2, 2, 2, 2, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0],
        "num_classes": 5,
        "verbose": -1,
        "seed": 0,
    }
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    results_weight = {}
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    lgb.train(params, lgb_X, num_boost_round=5, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_weight)])
    params["auc_mu_weights"] = []
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    results_no_weight = {}
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    lgb.train(
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        params, lgb_X, num_boost_round=5, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(results_no_weight)]
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    )
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    assert results_weight["training"]["auc_mu"][-1] != results_no_weight["training"]["auc_mu"][-1]
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600
def test_ranking_prediction_early_stopping():
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    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
    X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train"))
    q_train = np.loadtxt(str(rank_example_dir / "rank.train.query"))
    X_test, _ = load_svmlight_file(str(rank_example_dir / "rank.test"))
    params = {"objective": "rank_xendcg", "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, group=q_train, params=params)
    gbm = lgb.train(params, lgb_train, num_boost_round=50)

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    pred_parameter = {"pred_early_stop": True, "pred_early_stop_freq": 5, "pred_early_stop_margin": 1.5}
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    ret_early = gbm.predict(X_test, **pred_parameter)

    pred_parameter["pred_early_stop_margin"] = 5.5
    ret_early_more_strict = gbm.predict(X_test, **pred_parameter)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(ret_early, ret_early_more_strict)


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# Simulates position bias for a given ranking dataset.
# The ouput dataset is identical to the input one with the exception for the relevance labels.
# The new labels are generated according to an instance of a cascade user model:
# for each query, the user is simulated to be traversing the list of documents ranked by a baseline ranker
# (in our example it is simply the ordering by some feature correlated with relevance, e.g., 34)
# and clicks on that document (new_label=1) with some probability 'pclick' depending on its true relevance;
# at each position the user may stop the traversal with some probability pstop. For the non-clicked documents,
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# new_label=0. Thus the generated new labels are biased towards the baseline ranker.
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# The positions of the documents in the ranked lists produced by the baseline, are returned.
def simulate_position_bias(file_dataset_in, file_query_in, file_dataset_out, baseline_feature):
    # a mapping of a document's true relevance (defined on a 5-grade scale) into the probability of clicking it
    def get_pclick(label):
        if label == 0:
            return 0.4
        elif label == 1:
            return 0.6
        elif label == 2:
            return 0.7
        elif label == 3:
            return 0.8
        else:
            return 0.9
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    # an instantiation of a cascade model where the user stops with probability 0.2 after observing each document
    pstop = 0.2
643

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    f_dataset_in = open(file_dataset_in, "r")
    f_dataset_out = open(file_dataset_out, "w")
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    random.seed(10)
    positions_all = []
    for line in open(file_query_in):
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        docs_num = int(line)
650
        lines = []
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        index_values = []
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        positions = [0] * docs_num
        for index in range(docs_num):
            features = f_dataset_in.readline().split()
            lines.append(features)
            val = 0.0
            for feature_val in features:
658
                feature_val_split = feature_val.split(":")
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                if int(feature_val_split[0]) == baseline_feature:
                    val = float(feature_val_split[1])
            index_values.append([index, val])
        index_values.sort(key=lambda x: -x[1])
663
        stop = False
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        for pos in range(docs_num):
            index = index_values[pos][0]
            new_label = 0
            if not stop:
                label = int(lines[index][0])
                pclick = get_pclick(label)
                if random.random() < pclick:
671
                    new_label = 1
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                stop = random.random() < pstop
            lines[index][0] = str(new_label)
            positions[index] = pos
        for features in lines:
676
            f_dataset_out.write(" ".join(features) + "\n")
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        positions_all.extend(positions)
    f_dataset_out.close()
    return positions_all


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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Positions in learning to rank is not supported in CUDA version yet"
)
685
def test_ranking_with_position_information_with_file(tmp_path):
686
    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
687
    params = {
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        "objective": "lambdarank",
        "verbose": -1,
        "eval_at": [3],
        "metric": "ndcg",
        "bagging_freq": 1,
        "bagging_fraction": 0.9,
        "min_data_in_leaf": 50,
        "min_sum_hessian_in_leaf": 5.0,
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    }

    # simulate position bias for the train dataset and put the train dataset with biased labels to temp directory
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    positions = simulate_position_bias(
        str(rank_example_dir / "rank.train"),
        str(rank_example_dir / "rank.train.query"),
        str(tmp_path / "rank.train"),
        baseline_feature=34,
    )
    copyfile(str(rank_example_dir / "rank.train.query"), str(tmp_path / "rank.train.query"))
    copyfile(str(rank_example_dir / "rank.test"), str(tmp_path / "rank.test"))
    copyfile(str(rank_example_dir / "rank.test.query"), str(tmp_path / "rank.test.query"))
708

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    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
    gbm_baseline = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
712

713
    f_positions_out = open(str(tmp_path / "rank.train.position"), "w")
714
    for pos in positions:
715
        f_positions_out.write(str(pos) + "\n")
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    f_positions_out.close()

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    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
    gbm_unbiased_with_file = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
721

722
    # the performance of the unbiased LambdaMART should outperform the plain LambdaMART on the dataset with position bias
723
    assert gbm_baseline.best_score["valid_0"]["ndcg@3"] + 0.03 <= gbm_unbiased_with_file.best_score["valid_0"]["ndcg@3"]
724
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    # add extra row to position file
726
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    with open(str(tmp_path / "rank.train.position"), "a") as file:
        file.write("pos_1000\n")
728
        file.close()
729
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    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
731
    with pytest.raises(lgb.basic.LightGBMError, match=r"Positions size \(3006\) doesn't match data size"):
732
        lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
733
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@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Positions in learning to rank is not supported in CUDA version yet"
)
738
def test_ranking_with_position_information_with_dataset_constructor(tmp_path):
739
    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
740
    params = {
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751
        "objective": "lambdarank",
        "verbose": -1,
        "eval_at": [3],
        "metric": "ndcg",
        "bagging_freq": 1,
        "bagging_fraction": 0.9,
        "min_data_in_leaf": 50,
        "min_sum_hessian_in_leaf": 5.0,
        "num_threads": 1,
        "deterministic": True,
        "seed": 0,
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754
    }

    # simulate position bias for the train dataset and put the train dataset with biased labels to temp directory
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    positions = simulate_position_bias(
        str(rank_example_dir / "rank.train"),
        str(rank_example_dir / "rank.train.query"),
        str(tmp_path / "rank.train"),
        baseline_feature=34,
    )
    copyfile(str(rank_example_dir / "rank.train.query"), str(tmp_path / "rank.train.query"))
    copyfile(str(rank_example_dir / "rank.test"), str(tmp_path / "rank.test"))
    copyfile(str(rank_example_dir / "rank.test.query"), str(tmp_path / "rank.test.query"))
764

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767
    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
    gbm_baseline = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
768
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771

    positions = np.array(positions)

    # test setting positions through Dataset constructor with numpy array
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    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params, position=positions)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
    gbm_unbiased = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
775
776

    # the performance of the unbiased LambdaMART should outperform the plain LambdaMART on the dataset with position bias
777
    assert gbm_baseline.best_score["valid_0"]["ndcg@3"] + 0.03 <= gbm_unbiased.best_score["valid_0"]["ndcg@3"]
778
779
780

    if PANDAS_INSTALLED:
        # test setting positions through Dataset constructor with pandas Series
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786
        lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params, position=pd_Series(positions))
        lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
        gbm_unbiased_pandas_series = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
        assert (
            gbm_unbiased.best_score["valid_0"]["ndcg@3"] == gbm_unbiased_pandas_series.best_score["valid_0"]["ndcg@3"]
        )
787
788

    # test setting positions through set_position
789
790
    lgb_train = lgb.Dataset(str(tmp_path / "rank.train"), params=params)
    lgb_valid = [lgb_train.create_valid(str(tmp_path / "rank.test"))]
791
    lgb_train.set_position(positions)
792
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    gbm_unbiased_set_position = lgb.train(params, lgb_train, valid_sets=lgb_valid, num_boost_round=50)
    assert gbm_unbiased.best_score["valid_0"]["ndcg@3"] == gbm_unbiased_set_position.best_score["valid_0"]["ndcg@3"]
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797
798
799

    # test get_position works
    positions_from_get = lgb_train.get_position()
    np.testing.assert_array_equal(positions_from_get, positions)


800
801
def test_early_stopping():
    X, y = load_breast_cancer(return_X_y=True)
802
    params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1}
803
804
805
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
806
    valid_set_name = "valid_set"
807
    # no early stopping
808
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812
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814
815
    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=10,
        valid_sets=lgb_eval,
        valid_names=valid_set_name,
        callbacks=[lgb.early_stopping(stopping_rounds=5)],
    )
816
817
    assert gbm.best_iteration == 10
    assert valid_set_name in gbm.best_score
818
    assert "binary_logloss" in gbm.best_score[valid_set_name]
819
    # early stopping occurs
820
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823
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825
826
827
    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=40,
        valid_sets=lgb_eval,
        valid_names=valid_set_name,
        callbacks=[lgb.early_stopping(stopping_rounds=5)],
    )
828
829
    assert gbm.best_iteration <= 39
    assert valid_set_name in gbm.best_score
830
    assert "binary_logloss" in gbm.best_score[valid_set_name]
831
832


833
@pytest.mark.parametrize("use_valid", [True, False])
834
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842
def test_early_stopping_ignores_training_set(use_valid):
    x = np.linspace(-1, 1, 100)
    X = x.reshape(-1, 1)
    y = x**2
    X_train, X_valid = X[:80], X[80:]
    y_train, y_valid = y[:80], y[80:]
    train_ds = lgb.Dataset(X_train, y_train)
    valid_ds = lgb.Dataset(X_valid, y_valid)
    valid_sets = [train_ds]
843
    valid_names = ["train"]
844
845
    if use_valid:
        valid_sets.append(valid_ds)
846
        valid_names.append("valid")
847
848
849
850
    eval_result = {}

    def train_fn():
        return lgb.train(
851
            {"num_leaves": 5},
852
853
854
855
            train_ds,
            num_boost_round=2,
            valid_sets=valid_sets,
            valid_names=valid_names,
856
            callbacks=[lgb.early_stopping(1), lgb.record_evaluation(eval_result)],
857
        )
858

859
860
861
    if use_valid:
        bst = train_fn()
        assert bst.best_iteration == 1
862
863
        assert eval_result["train"]["l2"][1] < eval_result["train"]["l2"][0]  # train improved
        assert eval_result["valid"]["l2"][1] > eval_result["valid"]["l2"][0]  # valid didn't
864
    else:
865
        with pytest.warns(UserWarning, match="Only training set found, disabling early stopping."):
866
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868
869
870
            bst = train_fn()
        assert bst.current_iteration() == 2
        assert bst.best_iteration == 0


871
@pytest.mark.parametrize("first_metric_only", [True, False])
872
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875
def test_early_stopping_via_global_params(first_metric_only):
    X, y = load_breast_cancer(return_X_y=True)
    num_trees = 5
    params = {
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878
879
880
881
        "num_trees": num_trees,
        "objective": "binary",
        "metric": "None",
        "verbose": -1,
        "early_stopping_round": 2,
        "first_metric_only": first_metric_only,
882
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884
885
    }
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
886
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889
    valid_set_name = "valid_set"
    gbm = lgb.train(
        params, lgb_train, feval=[decreasing_metric, constant_metric], valid_sets=lgb_eval, valid_names=valid_set_name
    )
890
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    if first_metric_only:
        assert gbm.best_iteration == num_trees
    else:
        assert gbm.best_iteration == 1
    assert valid_set_name in gbm.best_score
895
896
    assert "decreasing_metric" in gbm.best_score[valid_set_name]
    assert "error" in gbm.best_score[valid_set_name]
897
898


899
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901
@pytest.mark.parametrize("first_only", [True, False])
@pytest.mark.parametrize("single_metric", [True, False])
@pytest.mark.parametrize("greater_is_better", [True, False])
902
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904
905
def test_early_stopping_min_delta(first_only, single_metric, greater_is_better):
    if single_metric and not first_only:
        pytest.skip("first_metric_only doesn't affect single metric.")
    metric2min_delta = {
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909
        "auc": 0.001,
        "binary_logloss": 0.01,
        "average_precision": 0.001,
        "mape": 0.01,
910
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912
    }
    if single_metric:
        if greater_is_better:
913
            metric = "auc"
914
        else:
915
            metric = "binary_logloss"
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    else:
        if first_only:
            if greater_is_better:
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                metric = ["auc", "binary_logloss"]
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            else:
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                metric = ["binary_logloss", "auc"]
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        else:
            if greater_is_better:
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                metric = ["auc", "average_precision"]
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            else:
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                metric = ["binary_logloss", "mape"]
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    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=0)
    train_ds = lgb.Dataset(X_train, y_train)
    valid_ds = lgb.Dataset(X_valid, y_valid, reference=train_ds)

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    params = {"objective": "binary", "metric": metric, "verbose": -1}
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    if isinstance(metric, str):
        min_delta = metric2min_delta[metric]
    elif first_only:
        min_delta = metric2min_delta[metric[0]]
    else:
        min_delta = [metric2min_delta[m] for m in metric]
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    train_kwargs = {
        "params": params,
        "train_set": train_ds,
        "num_boost_round": 50,
        "valid_sets": [train_ds, valid_ds],
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        "valid_names": ["training", "valid"],
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    }
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    # regular early stopping
    evals_result = {}
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    train_kwargs["callbacks"] = [
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        lgb.callback.early_stopping(10, first_only, verbose=False),
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        lgb.record_evaluation(evals_result),
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    ]
    bst = lgb.train(**train_kwargs)
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    scores = np.vstack(list(evals_result["valid"].values())).T
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    # positive min_delta
    delta_result = {}
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    train_kwargs["callbacks"] = [
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        lgb.callback.early_stopping(10, first_only, verbose=False, min_delta=min_delta),
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        lgb.record_evaluation(delta_result),
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    ]
    delta_bst = lgb.train(**train_kwargs)
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    delta_scores = np.vstack(list(delta_result["valid"].values())).T
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    if first_only:
        scores = scores[:, 0]
        delta_scores = delta_scores[:, 0]

    assert delta_bst.num_trees() < bst.num_trees()
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    np.testing.assert_allclose(scores[: len(delta_scores)], delta_scores)
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    last_score = delta_scores[-1]
    best_score = delta_scores[delta_bst.num_trees() - 1]
    if greater_is_better:
        assert np.less_equal(last_score, best_score + min_delta).any()
    else:
        assert np.greater_equal(last_score, best_score - min_delta).any()


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def test_early_stopping_can_be_triggered_via_custom_callback():
    X, y = make_synthetic_regression()

    def _early_stop_after_seventh_iteration(env):
        if env.iteration == 6:
            exc = lgb.EarlyStopException(
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                best_iteration=6, best_score=[("some_validation_set", "some_metric", 0.708, True)]
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            )
            raise exc

    bst = lgb.train(
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        params={"objective": "regression", "verbose": -1, "num_leaves": 2},
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        train_set=lgb.Dataset(X, label=y),
        num_boost_round=23,
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        callbacks=[_early_stop_after_seventh_iteration],
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    )
    assert bst.num_trees() == 7
    assert bst.best_score["some_validation_set"]["some_metric"] == 0.708
    assert bst.best_iteration == 7
    assert bst.current_iteration() == 7


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def test_continue_train():
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"objective": "regression", "metric": "l1", "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, free_raw_data=False)
    init_gbm = lgb.train(params, lgb_train, num_boost_round=20)
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    model_name = "model.txt"
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    init_gbm.save_model(model_name)
    evals_result = {}
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    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=30,
        valid_sets=lgb_eval,
        # test custom eval metrics
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        feval=(lambda p, d: ("custom_mae", mean_absolute_error(p, d.get_label()), False)),
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        callbacks=[lgb.record_evaluation(evals_result)],
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        init_model="model.txt",
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    )
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    ret = mean_absolute_error(y_test, gbm.predict(X_test))
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    assert ret < 13.6
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    assert evals_result["valid_0"]["l1"][-1] == pytest.approx(ret)
    np.testing.assert_allclose(evals_result["valid_0"]["l1"], evals_result["valid_0"]["custom_mae"])
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def test_continue_train_reused_dataset():
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    X, y = make_synthetic_regression()
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    params = {"objective": "regression", "verbose": -1}
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    lgb_train = lgb.Dataset(X, y, free_raw_data=False)
    init_gbm = lgb.train(params, lgb_train, num_boost_round=5)
    init_gbm_2 = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm)
    init_gbm_3 = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm_2)
    gbm = lgb.train(params, lgb_train, num_boost_round=5, init_model=init_gbm_3)
    assert gbm.current_iteration() == 20


def test_continue_train_dart():
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    X, y = make_synthetic_regression()
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    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"boosting_type": "dart", "objective": "regression", "metric": "l1", "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, free_raw_data=False)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, free_raw_data=False)
    init_gbm = lgb.train(params, lgb_train, num_boost_round=50)
    evals_result = {}
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    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=50,
        valid_sets=lgb_eval,
        callbacks=[lgb.record_evaluation(evals_result)],
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        init_model=init_gbm,
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    )
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    ret = mean_absolute_error(y_test, gbm.predict(X_test))
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    assert ret < 13.6
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    assert evals_result["valid_0"]["l1"][-1] == pytest.approx(ret)
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def test_continue_train_multiclass():
    X, y = load_iris(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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    params = {"objective": "multiclass", "metric": "multi_logloss", "num_class": 3, "verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train, params=params, free_raw_data=False)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, params=params, free_raw_data=False)
    init_gbm = lgb.train(params, lgb_train, num_boost_round=20)
    evals_result = {}
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    gbm = lgb.train(
        params,
        lgb_train,
        num_boost_round=30,
        valid_sets=lgb_eval,
        callbacks=[lgb.record_evaluation(evals_result)],
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        init_model=init_gbm,
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    )
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    ret = multi_logloss(y_test, gbm.predict(X_test))
    assert ret < 0.1
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    assert evals_result["valid_0"]["multi_logloss"][-1] == pytest.approx(ret)
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def test_cv():
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    X_train, y_train = make_synthetic_regression()
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    params = {"verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train)
    # shuffle = False, override metric in params
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    params_with_metric = {"metric": "l2", "verbose": -1}
    cv_res = lgb.cv(
        params_with_metric, lgb_train, num_boost_round=10, nfold=3, stratified=False, shuffle=False, metrics="l1"
    )
    assert "valid l1-mean" in cv_res
    assert "valid l2-mean" not in cv_res
    assert len(cv_res["valid l1-mean"]) == 10
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    # shuffle = True, callbacks
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    cv_res = lgb.cv(
        params,
        lgb_train,
        num_boost_round=10,
        nfold=3,
        stratified=False,
        shuffle=True,
        metrics="l1",
        callbacks=[lgb.reset_parameter(learning_rate=lambda i: 0.1 - 0.001 * i)],
    )
    assert "valid l1-mean" in cv_res
    assert len(cv_res["valid l1-mean"]) == 10
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    # enable display training loss
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    cv_res = lgb.cv(
        params_with_metric,
        lgb_train,
        num_boost_round=10,
        nfold=3,
        stratified=False,
        shuffle=False,
        metrics="l1",
        eval_train_metric=True,
    )
    assert "train l1-mean" in cv_res
    assert "valid l1-mean" in cv_res
    assert "train l2-mean" not in cv_res
    assert "valid l2-mean" not in cv_res
    assert len(cv_res["train l1-mean"]) == 10
    assert len(cv_res["valid l1-mean"]) == 10
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    # self defined folds
    tss = TimeSeriesSplit(3)
    folds = tss.split(X_train)
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    cv_res_gen = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=folds)
    cv_res_obj = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=tss)
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    np.testing.assert_allclose(cv_res_gen["valid l2-mean"], cv_res_obj["valid l2-mean"])
Andrew Ziem's avatar
Andrew Ziem committed
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    # LambdaRank
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    rank_example_dir = Path(__file__).absolute().parents[2] / "examples" / "lambdarank"
    X_train, y_train = load_svmlight_file(str(rank_example_dir / "rank.train"))
    q_train = np.loadtxt(str(rank_example_dir / "rank.train.query"))
    params_lambdarank = {"objective": "lambdarank", "verbose": -1, "eval_at": 3}
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    lgb_train = lgb.Dataset(X_train, y_train, group=q_train)
    # ... with l2 metric
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    cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, metrics="l2")
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    assert len(cv_res_lambda) == 2
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    assert not np.isnan(cv_res_lambda["valid l2-mean"]).any()
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    # ... with NDCG (default) metric
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    cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3)
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    assert len(cv_res_lambda) == 2
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    assert not np.isnan(cv_res_lambda["valid ndcg@3-mean"]).any()
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    # self defined folds with lambdarank
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    cv_res_lambda_obj = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, folds=GroupKFold(n_splits=3))
    np.testing.assert_allclose(cv_res_lambda["valid ndcg@3-mean"], cv_res_lambda_obj["valid ndcg@3-mean"])
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def test_cv_works_with_init_model(tmp_path):
    X, y = make_synthetic_regression()
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    params = {"objective": "regression", "verbose": -1}
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    num_train_rounds = 2
    lgb_train = lgb.Dataset(X, y, free_raw_data=False)
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    bst = lgb.train(params=params, train_set=lgb_train, num_boost_round=num_train_rounds)
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    preds_raw = bst.predict(X, raw_score=True)
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    model_path_txt = str(tmp_path / "lgb.model")
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    bst.save_model(model_path_txt)

    num_cv_rounds = 5
    cv_kwargs = {
        "num_boost_round": num_cv_rounds,
        "nfold": 3,
        "stratified": False,
        "shuffle": False,
        "seed": 708,
        "return_cvbooster": True,
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        "params": params,
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    }

    # init_model from an in-memory Booster
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    cv_res = lgb.cv(train_set=lgb_train, init_model=bst, **cv_kwargs)
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    cv_bst_w_in_mem_init_model = cv_res["cvbooster"]
    assert cv_bst_w_in_mem_init_model.current_iteration() == [num_train_rounds + num_cv_rounds] * 3
    for booster in cv_bst_w_in_mem_init_model.boosters:
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        np.testing.assert_allclose(preds_raw, booster.predict(X, raw_score=True, num_iteration=num_train_rounds))
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    # init_model from a text file
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    cv_res = lgb.cv(train_set=lgb_train, init_model=model_path_txt, **cv_kwargs)
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    cv_bst_w_file_init_model = cv_res["cvbooster"]
    assert cv_bst_w_file_init_model.current_iteration() == [num_train_rounds + num_cv_rounds] * 3
    for booster in cv_bst_w_file_init_model.boosters:
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        np.testing.assert_allclose(preds_raw, booster.predict(X, raw_score=True, num_iteration=num_train_rounds))
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    # predictions should be identical
    for i in range(3):
        np.testing.assert_allclose(
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            cv_bst_w_in_mem_init_model.boosters[i].predict(X), cv_bst_w_file_init_model.boosters[i].predict(X)
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        )


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def test_cvbooster():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "metric": "binary_logloss",
        "verbose": -1,
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    }
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    nfold = 3
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    lgb_train = lgb.Dataset(X_train, y_train)
    # with early stopping
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    cv_res = lgb.cv(
        params,
        lgb_train,
        num_boost_round=25,
        nfold=nfold,
        callbacks=[lgb.early_stopping(stopping_rounds=5)],
        return_cvbooster=True,
    )
    assert "cvbooster" in cv_res
    cvb = cv_res["cvbooster"]
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    assert isinstance(cvb, lgb.CVBooster)
    assert isinstance(cvb.boosters, list)
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    assert len(cvb.boosters) == nfold
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    assert all(isinstance(bst, lgb.Booster) for bst in cvb.boosters)
    assert cvb.best_iteration > 0
    # predict by each fold booster
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    preds = cvb.predict(X_test)
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    assert isinstance(preds, list)
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    assert len(preds) == nfold
    # check that each booster predicted using the best iteration
    for fold_preds, bst in zip(preds, cvb.boosters):
        assert bst.best_iteration == cvb.best_iteration
        expected = bst.predict(X_test, num_iteration=cvb.best_iteration)
        np.testing.assert_allclose(fold_preds, expected)
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    # fold averaging
    avg_pred = np.mean(preds, axis=0)
    ret = log_loss(y_test, avg_pred)
    assert ret < 0.13
    # without early stopping
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    cv_res = lgb.cv(params, lgb_train, num_boost_round=20, nfold=3, return_cvbooster=True)
    cvb = cv_res["cvbooster"]
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    assert cvb.best_iteration == -1
    preds = cvb.predict(X_test)
    avg_pred = np.mean(preds, axis=0)
    ret = log_loss(y_test, avg_pred)
    assert ret < 0.15


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def test_cvbooster_save_load(tmp_path):
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "metric": "binary_logloss",
        "verbose": -1,
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    }
    nfold = 3
    lgb_train = lgb.Dataset(X_train, y_train)

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    cv_res = lgb.cv(
        params,
        lgb_train,
        num_boost_round=10,
        nfold=nfold,
        callbacks=[lgb.early_stopping(stopping_rounds=5)],
        return_cvbooster=True,
    )
    cvbooster = cv_res["cvbooster"]
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    preds = cvbooster.predict(X_test)
    best_iteration = cvbooster.best_iteration

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    model_path_txt = str(tmp_path / "lgb.model")
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    cvbooster.save_model(model_path_txt)
    model_string = cvbooster.model_to_string()
    del cvbooster

    cvbooster_from_txt_file = lgb.CVBooster(model_file=model_path_txt)
    cvbooster_from_string = lgb.CVBooster().model_from_string(model_string)
    for cvbooster_loaded in [cvbooster_from_txt_file, cvbooster_from_string]:
        assert best_iteration == cvbooster_loaded.best_iteration
        np.testing.assert_array_equal(preds, cvbooster_loaded.predict(X_test))


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@pytest.mark.parametrize("serializer", SERIALIZERS)
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def test_cvbooster_picklable(serializer):
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, _ = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "metric": "binary_logloss",
        "verbose": -1,
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    }
    nfold = 3
    lgb_train = lgb.Dataset(X_train, y_train)

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    cv_res = lgb.cv(
        params,
        lgb_train,
        num_boost_round=10,
        nfold=nfold,
        callbacks=[lgb.early_stopping(stopping_rounds=5)],
        return_cvbooster=True,
    )
    cvbooster = cv_res["cvbooster"]
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    preds = cvbooster.predict(X_test)
    best_iteration = cvbooster.best_iteration

    cvbooster_from_disk = pickle_and_unpickle_object(obj=cvbooster, serializer=serializer)
    del cvbooster

    assert best_iteration == cvbooster_from_disk.best_iteration

    preds_from_disk = cvbooster_from_disk.predict(X_test)
    np.testing.assert_array_equal(preds, preds_from_disk)


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def test_feature_name():
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    X_train, y_train = make_synthetic_regression()
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    params = {"verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train)
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    feature_names = [f"f_{i}" for i in range(X_train.shape[-1])]
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    gbm = lgb.train(params, lgb_train, num_boost_round=5, feature_name=feature_names)
    assert feature_names == gbm.feature_name()
    # test feature_names with whitespaces
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    feature_names_with_space = [f"f {i}" for i in range(X_train.shape[-1])]
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    gbm = lgb.train(params, lgb_train, num_boost_round=5, feature_name=feature_names_with_space)
    assert feature_names == gbm.feature_name()


def test_feature_name_with_non_ascii():
    X_train = np.random.normal(size=(100, 4))
    y_train = np.random.random(100)
    # This has non-ascii strings.
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    feature_names = ["F_零", "F_一", "F_二", "F_三"]
    params = {"verbose": -1}
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    lgb_train = lgb.Dataset(X_train, y_train)

    gbm = lgb.train(params, lgb_train, num_boost_round=5, feature_name=feature_names)
    assert feature_names == gbm.feature_name()
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    gbm.save_model("lgb.model")
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    gbm2 = lgb.Booster(model_file="lgb.model")
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    assert feature_names == gbm2.feature_name()


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def test_parameters_are_loaded_from_model_file(tmp_path, capsys):
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    X = np.hstack([np.random.rand(100, 1), np.random.randint(0, 5, (100, 2))])
    y = np.random.rand(100)
    ds = lgb.Dataset(X, y)
    params = {
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        "bagging_fraction": 0.8,
        "bagging_freq": 2,
        "boosting": "rf",
        "feature_contri": [0.5, 0.5, 0.5],
        "feature_fraction": 0.7,
        "boost_from_average": False,
        "interaction_constraints": [[0, 1], [0]],
        "metric": ["l2", "rmse"],
        "num_leaves": 5,
        "num_threads": 1,
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    }
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    model_file = tmp_path / "model.txt"
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    orig_bst = lgb.train(params, ds, num_boost_round=1, categorical_feature=[1, 2])
    orig_bst.save_model(model_file)
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    with model_file.open("rt") as f:
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        model_contents = f.readlines()
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    params_start = model_contents.index("parameters:\n")
    model_contents.insert(params_start + 1, "[max_conflict_rate: 0]\n")
    with model_file.open("wt") as f:
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        f.writelines(model_contents)
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    bst = lgb.Booster(model_file=model_file)
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    expected_msg = "[LightGBM] [Warning] Ignoring unrecognized parameter 'max_conflict_rate' found in model string."
    stdout = capsys.readouterr().out
    assert expected_msg in stdout
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    set_params = {k: bst.params[k] for k in params.keys()}
    assert set_params == params
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    assert bst.params["categorical_feature"] == [1, 2]
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    # check that passing parameters to the constructor raises warning and ignores them
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    with pytest.warns(UserWarning, match="Ignoring params argument"):
        bst2 = lgb.Booster(params={"num_leaves": 7}, model_file=model_file)
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    assert bst.params == bst2.params

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    # check inference isn't affected by unknown parameter
    orig_preds = orig_bst.predict(X)
    preds = bst.predict(X)
    np.testing.assert_allclose(preds, orig_preds)

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def test_save_load_copy_pickle():
    def train_and_predict(init_model=None, return_model=False):
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        X, y = make_synthetic_regression()
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        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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        params = {"objective": "regression", "metric": "l2", "verbose": -1}
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        lgb_train = lgb.Dataset(X_train, y_train)
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        gbm_template = lgb.train(params, lgb_train, num_boost_round=10, init_model=init_model)
        return gbm_template if return_model else mean_squared_error(y_test, gbm_template.predict(X_test))

    gbm = train_and_predict(return_model=True)
    ret_origin = train_and_predict(init_model=gbm)
    other_ret = []
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    gbm.save_model("lgb.model")
    with open("lgb.model") as f:  # check all params are logged into model file correctly
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        assert f.read().find("[num_iterations: 10]") != -1
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    other_ret.append(train_and_predict(init_model="lgb.model"))
    gbm_load = lgb.Booster(model_file="lgb.model")
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    other_ret.append(train_and_predict(init_model=gbm_load))
    other_ret.append(train_and_predict(init_model=copy.copy(gbm)))
    other_ret.append(train_and_predict(init_model=copy.deepcopy(gbm)))
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    with open("lgb.pkl", "wb") as f:
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        pickle.dump(gbm, f)
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    with open("lgb.pkl", "rb") as f:
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        gbm_pickle = pickle.load(f)
    other_ret.append(train_and_predict(init_model=gbm_pickle))
    gbm_pickles = pickle.loads(pickle.dumps(gbm))
    other_ret.append(train_and_predict(init_model=gbm_pickles))
    for ret in other_ret:
        assert ret_origin == pytest.approx(ret)


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def test_all_expected_params_are_written_out_to_model_text(tmp_path):
    X, y = make_synthetic_regression()
    params = {
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        "objective": "mape",
        "metric": ["l2", "mae"],
        "seed": 708,
        "data_sample_strategy": "bagging",
        "sub_row": 0.8234,
        "verbose": -1,
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    }
    dtrain = lgb.Dataset(data=X, label=y)
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    gbm = lgb.train(params=params, train_set=dtrain, num_boost_round=3)
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    model_txt_from_memory = gbm.model_to_string()
    model_file = tmp_path / "out.model"
    gbm.save_model(filename=model_file)
    with open(model_file, "r") as f:
        model_txt_from_file = f.read()

    assert model_txt_from_memory == model_txt_from_file

    # entries whose values should reflect params passed to lgb.train()
    non_default_param_entries = [
        "[objective: mape]",
        # 'l1' was passed in with alias 'mae'
        "[metric: l2,l1]",
        "[data_sample_strategy: bagging]",
        "[seed: 708]",
        # NOTE: this was passed in with alias 'sub_row'
        "[bagging_fraction: 0.8234]",
        "[num_iterations: 3]",
    ]

    # entries with default values of params
    default_param_entries = [
        "[boosting: gbdt]",
        "[tree_learner: serial]",
        "[data: ]",
        "[valid: ]",
        "[learning_rate: 0.1]",
        "[num_leaves: 31]",
        "[num_threads: 0]",
        "[deterministic: 0]",
        "[histogram_pool_size: -1]",
        "[max_depth: -1]",
        "[min_data_in_leaf: 20]",
        "[min_sum_hessian_in_leaf: 0.001]",
        "[pos_bagging_fraction: 1]",
        "[neg_bagging_fraction: 1]",
        "[bagging_freq: 0]",
        "[bagging_seed: 15415]",
        "[feature_fraction: 1]",
        "[feature_fraction_bynode: 1]",
        "[feature_fraction_seed: 32671]",
        "[extra_trees: 0]",
        "[extra_seed: 6642]",
        "[early_stopping_round: 0]",
        "[first_metric_only: 0]",
        "[max_delta_step: 0]",
        "[lambda_l1: 0]",
        "[lambda_l2: 0]",
        "[linear_lambda: 0]",
        "[min_gain_to_split: 0]",
        "[drop_rate: 0.1]",
        "[max_drop: 50]",
        "[skip_drop: 0.5]",
        "[xgboost_dart_mode: 0]",
        "[uniform_drop: 0]",
        "[drop_seed: 20623]",
        "[top_rate: 0.2]",
        "[other_rate: 0.1]",
        "[min_data_per_group: 100]",
        "[max_cat_threshold: 32]",
        "[cat_l2: 10]",
        "[cat_smooth: 10]",
        "[max_cat_to_onehot: 4]",
        "[top_k: 20]",
        "[monotone_constraints: ]",
        "[monotone_constraints_method: basic]",
        "[monotone_penalty: 0]",
        "[feature_contri: ]",
        "[forcedsplits_filename: ]",
        "[refit_decay_rate: 0.9]",
        "[cegb_tradeoff: 1]",
        "[cegb_penalty_split: 0]",
        "[cegb_penalty_feature_lazy: ]",
        "[cegb_penalty_feature_coupled: ]",
        "[path_smooth: 0]",
        "[interaction_constraints: ]",
        "[verbosity: -1]",
        "[saved_feature_importance_type: 0]",
        "[use_quantized_grad: 0]",
        "[num_grad_quant_bins: 4]",
        "[quant_train_renew_leaf: 0]",
        "[stochastic_rounding: 1]",
        "[linear_tree: 0]",
        "[max_bin: 255]",
        "[max_bin_by_feature: ]",
        "[min_data_in_bin: 3]",
        "[bin_construct_sample_cnt: 200000]",
        "[data_random_seed: 2350]",
        "[is_enable_sparse: 1]",
        "[enable_bundle: 1]",
        "[use_missing: 1]",
        "[zero_as_missing: 0]",
        "[feature_pre_filter: 1]",
        "[pre_partition: 0]",
        "[two_round: 0]",
        "[header: 0]",
        "[label_column: ]",
        "[weight_column: ]",
        "[group_column: ]",
        "[ignore_column: ]",
        "[categorical_feature: ]",
        "[forcedbins_filename: ]",
        "[precise_float_parser: 0]",
        "[parser_config_file: ]",
        "[objective_seed: 4309]",
        "[num_class: 1]",
        "[is_unbalance: 0]",
        "[scale_pos_weight: 1]",
        "[sigmoid: 1]",
        "[boost_from_average: 1]",
        "[reg_sqrt: 0]",
        "[alpha: 0.9]",
        "[fair_c: 1]",
        "[poisson_max_delta_step: 0.7]",
        "[tweedie_variance_power: 1.5]",
        "[lambdarank_truncation_level: 30]",
        "[lambdarank_norm: 1]",
        "[label_gain: ]",
        "[lambdarank_position_bias_regularization: 0]",
        "[eval_at: ]",
        "[multi_error_top_k: 1]",
        "[auc_mu_weights: ]",
        "[num_machines: 1]",
        "[local_listen_port: 12400]",
        "[time_out: 120]",
        "[machine_list_filename: ]",
        "[machines: ]",
        "[gpu_platform_id: -1]",
        "[gpu_device_id: -1]",
        "[num_gpu: 1]",
    ]
    all_param_entries = non_default_param_entries + default_param_entries

    # add device-specific entries
    #
    # passed-in force_col_wise / force_row_wise parameters are ignored on CUDA and GPU builds...
    # https://github.com/microsoft/LightGBM/blob/1d7ee63686272bceffd522284127573b511df6be/src/io/config.cpp#L375-L377
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    if getenv("TASK", "") == "cuda":
        device_entries = ["[force_col_wise: 0]", "[force_row_wise: 1]", "[device_type: cuda]", "[gpu_use_dp: 1]"]
    elif getenv("TASK", "") == "gpu":
        device_entries = ["[force_col_wise: 1]", "[force_row_wise: 0]", "[device_type: gpu]", "[gpu_use_dp: 0]"]
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    else:
1567
        device_entries = ["[force_col_wise: 0]", "[force_row_wise: 0]", "[device_type: cpu]", "[gpu_use_dp: 0]"]
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    all_param_entries += device_entries

    # check that model text has all expected param entries
    for param_str in all_param_entries:
        assert param_str in model_txt_from_file
        assert param_str in model_txt_from_memory

    # since Booster.model_to_string() is used when pickling, check that parameters all
    # roundtrip pickling successfully too
    gbm_pkl = pickle_and_unpickle_object(gbm, serializer="joblib")
    model_txt_from_memory = gbm_pkl.model_to_string()
    model_file = tmp_path / "out-pkl.model"
    gbm_pkl.save_model(filename=model_file)
    with open(model_file, "r") as f:
        model_txt_from_file = f.read()

    for param_str in all_param_entries:
        assert param_str in model_txt_from_file
        assert param_str in model_txt_from_memory


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def test_pandas_categorical():
    pd = pytest.importorskip("pandas")
    np.random.seed(42)  # sometimes there is no difference how cols are treated (cat or not cat)
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    X = pd.DataFrame(
        {
            "A": np.random.permutation(["a", "b", "c", "d"] * 75),  # str
            "B": np.random.permutation([1, 2, 3] * 100),  # int
            "C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
            "D": np.random.permutation([True, False] * 150),  # bool
            "E": pd.Categorical(np.random.permutation(["z", "y", "x", "w", "v"] * 60), ordered=True),
        }
    )  # str and ordered categorical
1602
    y = np.random.permutation([0, 1] * 150)
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    X_test = pd.DataFrame(
        {
            "A": np.random.permutation(["a", "b", "e"] * 20),  # unseen category
            "B": np.random.permutation([1, 3] * 30),
            "C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
            "D": np.random.permutation([True, False] * 30),
            "E": pd.Categorical(np.random.permutation(["z", "y"] * 30), ordered=True),
        }
    )
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    np.random.seed()  # reset seed
    cat_cols_actual = ["A", "B", "C", "D"]
    cat_cols_to_store = cat_cols_actual + ["E"]
1615
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    X[cat_cols_actual] = X[cat_cols_actual].astype("category")
    X_test[cat_cols_actual] = X_test[cat_cols_actual].astype("category")
1617
    cat_values = [X[col].cat.categories.tolist() for col in cat_cols_to_store]
1618
    params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1}
1619
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    lgb_train = lgb.Dataset(X, y)
    gbm0 = lgb.train(params, lgb_train, num_boost_round=10)
    pred0 = gbm0.predict(X_test)
1622
    assert lgb_train.categorical_feature == "auto"
1623
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    lgb_train = lgb.Dataset(X, pd.DataFrame(y))  # also test that label can be one-column pd.DataFrame
    gbm1 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=[0])
    pred1 = gbm1.predict(X_test)
    assert lgb_train.categorical_feature == [0]
    lgb_train = lgb.Dataset(X, pd.Series(y))  # also test that label can be pd.Series
1628
    gbm2 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=["A"])
1629
    pred2 = gbm2.predict(X_test)
1630
    assert lgb_train.categorical_feature == ["A"]
1631
    lgb_train = lgb.Dataset(X, y)
1632
    gbm3 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=["A", "B", "C", "D"])
1633
    pred3 = gbm3.predict(X_test)
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    assert lgb_train.categorical_feature == ["A", "B", "C", "D"]
    gbm3.save_model("categorical.model")
    gbm4 = lgb.Booster(model_file="categorical.model")
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    pred4 = gbm4.predict(X_test)
    model_str = gbm4.model_to_string()
1639
    gbm4.model_from_string(model_str)
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    pred5 = gbm4.predict(X_test)
    gbm5 = lgb.Booster(model_str=model_str)
    pred6 = gbm5.predict(X_test)
    lgb_train = lgb.Dataset(X, y)
1644
    gbm6 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=["A", "B", "C", "D", "E"])
1645
    pred7 = gbm6.predict(X_test)
1646
    assert lgb_train.categorical_feature == ["A", "B", "C", "D", "E"]
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    lgb_train = lgb.Dataset(X, y)
    gbm7 = lgb.train(params, lgb_train, num_boost_round=10, categorical_feature=[])
    pred8 = gbm7.predict(X_test)
    assert lgb_train.categorical_feature == []
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred1)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred2)
    np.testing.assert_allclose(pred1, pred2)
    np.testing.assert_allclose(pred0, pred3)
    np.testing.assert_allclose(pred0, pred4)
    np.testing.assert_allclose(pred0, pred5)
    np.testing.assert_allclose(pred0, pred6)
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(pred0, pred7)  # ordered cat features aren't treated as cat features by default
    with pytest.raises(AssertionError):
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        np.testing.assert_allclose(pred0, pred8)
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    assert gbm0.pandas_categorical == cat_values
    assert gbm1.pandas_categorical == cat_values
    assert gbm2.pandas_categorical == cat_values
    assert gbm3.pandas_categorical == cat_values
    assert gbm4.pandas_categorical == cat_values
    assert gbm5.pandas_categorical == cat_values
    assert gbm6.pandas_categorical == cat_values
    assert gbm7.pandas_categorical == cat_values


def test_pandas_sparse():
    pd = pytest.importorskip("pandas")
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    X = pd.DataFrame(
        {
            "A": pd.arrays.SparseArray(np.random.permutation([0, 1, 2] * 100)),
            "B": pd.arrays.SparseArray(np.random.permutation([0.0, 0.1, 0.2, -0.1, 0.2] * 60)),
            "C": pd.arrays.SparseArray(np.random.permutation([True, False] * 150)),
        }
    )
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    y = pd.Series(pd.arrays.SparseArray(np.random.permutation([0, 1] * 150)))
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    X_test = pd.DataFrame(
        {
            "A": pd.arrays.SparseArray(np.random.permutation([0, 2] * 30)),
            "B": pd.arrays.SparseArray(np.random.permutation([0.0, 0.1, 0.2, -0.1] * 15)),
            "C": pd.arrays.SparseArray(np.random.permutation([True, False] * 30)),
        }
    )
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    for dtype in pd.concat([X.dtypes, X_test.dtypes, pd.Series(y.dtypes)]):
        assert pd.api.types.is_sparse(dtype)
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    params = {"objective": "binary", "verbose": -1}
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    lgb_train = lgb.Dataset(X, y)
    gbm = lgb.train(params, lgb_train, num_boost_round=10)
    pred_sparse = gbm.predict(X_test, raw_score=True)
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    if hasattr(X_test, "sparse"):
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        pred_dense = gbm.predict(X_test.sparse.to_dense(), raw_score=True)
    else:
        pred_dense = gbm.predict(X_test.to_dense(), raw_score=True)
    np.testing.assert_allclose(pred_sparse, pred_dense)


def test_reference_chain():
    X = np.random.normal(size=(100, 2))
    y = np.random.normal(size=100)
    tmp_dat = lgb.Dataset(X, y)
    # take subsets and train
    tmp_dat_train = tmp_dat.subset(np.arange(80))
    tmp_dat_val = tmp_dat.subset(np.arange(80, 100)).subset(np.arange(18))
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    params = {"objective": "regression_l2", "metric": "rmse"}
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    evals_result = {}
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    lgb.train(
        params,
        tmp_dat_train,
        num_boost_round=20,
        valid_sets=[tmp_dat_train, tmp_dat_val],
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        callbacks=[lgb.record_evaluation(evals_result)],
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    )
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    assert len(evals_result["training"]["rmse"]) == 20
    assert len(evals_result["valid_1"]["rmse"]) == 20
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def test_contribs():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "metric": "binary_logloss",
        "verbose": -1,
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    }
    lgb_train = lgb.Dataset(X_train, y_train)
    gbm = lgb.train(params, lgb_train, num_boost_round=20)

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    assert (
        np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(gbm.predict(X_test, pred_contrib=True), axis=1))
        < 1e-4
    )
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def test_contribs_sparse():
    n_features = 20
    n_samples = 100
    # generate CSR sparse dataset
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    X, y = make_multilabel_classification(
        n_samples=n_samples, sparse=True, n_features=n_features, n_classes=1, n_labels=2
    )
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    y = y.flatten()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "binary",
        "verbose": -1,
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    }
    lgb_train = lgb.Dataset(X_train, y_train)
    gbm = lgb.train(params, lgb_train, num_boost_round=20)
    contribs_csr = gbm.predict(X_test, pred_contrib=True)
    assert isspmatrix_csr(contribs_csr)
    # convert data to dense and get back same contribs
    contribs_dense = gbm.predict(X_test.toarray(), pred_contrib=True)
    # validate the values are the same
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    if platform.machine() == "aarch64":
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        np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense, rtol=1, atol=1e-12)
    else:
        np.testing.assert_allclose(contribs_csr.toarray(), contribs_dense)
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    assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(contribs_dense, axis=1)) < 1e-4
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    # validate using CSC matrix
    X_test_csc = X_test.tocsc()
    contribs_csc = gbm.predict(X_test_csc, pred_contrib=True)
    assert isspmatrix_csc(contribs_csc)
    # validate the values are the same
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    if platform.machine() == "aarch64":
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        np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense, rtol=1, atol=1e-12)
    else:
        np.testing.assert_allclose(contribs_csc.toarray(), contribs_dense)
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def test_contribs_sparse_multiclass():
    n_features = 20
    n_samples = 100
    n_labels = 4
    # generate CSR sparse dataset
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    X, y = make_multilabel_classification(
        n_samples=n_samples, sparse=True, n_features=n_features, n_classes=1, n_labels=n_labels
    )
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    y = y.flatten()
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
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        "objective": "multiclass",
        "num_class": n_labels,
        "verbose": -1,
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    }
    lgb_train = lgb.Dataset(X_train, y_train)
    gbm = lgb.train(params, lgb_train, num_boost_round=20)
    contribs_csr = gbm.predict(X_test, pred_contrib=True)
    assert isinstance(contribs_csr, list)
    for perclass_contribs_csr in contribs_csr:
        assert isspmatrix_csr(perclass_contribs_csr)
    # convert data to dense and get back same contribs
    contribs_dense = gbm.predict(X_test.toarray(), pred_contrib=True)
    # validate the values are the same
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    contribs_csr_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csr]), 0, 1)
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    contribs_csr_arr_re = contribs_csr_array.reshape(
        (contribs_csr_array.shape[0], contribs_csr_array.shape[1] * contribs_csr_array.shape[2])
    )
    if platform.machine() == "aarch64":
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        np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense, rtol=1, atol=1e-12)
    else:
        np.testing.assert_allclose(contribs_csr_arr_re, contribs_dense)
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    contribs_dense_re = contribs_dense.reshape(contribs_csr_array.shape)
    assert np.linalg.norm(gbm.predict(X_test, raw_score=True) - np.sum(contribs_dense_re, axis=2)) < 1e-4
    # validate using CSC matrix
    X_test_csc = X_test.tocsc()
    contribs_csc = gbm.predict(X_test_csc, pred_contrib=True)
    assert isinstance(contribs_csc, list)
    for perclass_contribs_csc in contribs_csc:
        assert isspmatrix_csc(perclass_contribs_csc)
    # validate the values are the same
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    contribs_csc_array = np.swapaxes(np.array([sparse_array.toarray() for sparse_array in contribs_csc]), 0, 1)
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    contribs_csc_array = contribs_csc_array.reshape(
        (contribs_csc_array.shape[0], contribs_csc_array.shape[1] * contribs_csc_array.shape[2])
    )
    if platform.machine() == "aarch64":
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        np.testing.assert_allclose(contribs_csc_array, contribs_dense, rtol=1, atol=1e-12)
    else:
        np.testing.assert_allclose(contribs_csc_array, contribs_dense)
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@pytest.mark.skipif(psutil.virtual_memory().available / 1024 / 1024 / 1024 < 3, reason="not enough RAM")
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def test_int32_max_sparse_contribs():
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    params = {"objective": "binary"}
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    train_features = np.random.rand(100, 1000)
    train_targets = [0] * 50 + [1] * 50
    lgb_train = lgb.Dataset(train_features, train_targets)
    gbm = lgb.train(params, lgb_train, num_boost_round=2)
    csr_input_shape = (3000000, 1000)
    test_features = csr_matrix(csr_input_shape)
    for i in range(0, csr_input_shape[0], csr_input_shape[0] // 6):
        for j in range(0, 1000, 100):
            test_features[i, j] = random.random()
    y_pred_csr = gbm.predict(test_features, pred_contrib=True)
    # Note there is an extra column added to the output for the expected value
    csr_output_shape = (csr_input_shape[0], csr_input_shape[1] + 1)
    assert y_pred_csr.shape == csr_output_shape
    y_pred_csc = gbm.predict(test_features.tocsc(), pred_contrib=True)
    # Note output CSC shape should be same as CSR output shape
    assert y_pred_csc.shape == csr_output_shape


def test_sliced_data():
    def train_and_get_predictions(features, labels):
        dataset = lgb.Dataset(features, label=labels)
        lgb_params = {
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            "application": "binary",
            "verbose": -1,
            "min_data": 5,
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        }
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        gbm = lgb.train(
            params=lgb_params,
            train_set=dataset,
            num_boost_round=10,
        )
        return gbm.predict(features)

    num_samples = 100
    features = np.random.rand(num_samples, 5)
    positive_samples = int(num_samples * 0.25)
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    labels = np.append(
        np.ones(positive_samples, dtype=np.float32), np.zeros(num_samples - positive_samples, dtype=np.float32)
    )
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    # test sliced labels
    origin_pred = train_and_get_predictions(features, labels)
    stacked_labels = np.column_stack((labels, np.ones(num_samples, dtype=np.float32)))
    sliced_labels = stacked_labels[:, 0]
    sliced_pred = train_and_get_predictions(features, sliced_labels)
    np.testing.assert_allclose(origin_pred, sliced_pred)
    # append some columns
    stacked_features = np.column_stack((np.ones(num_samples, dtype=np.float32), features))
    stacked_features = np.column_stack((np.ones(num_samples, dtype=np.float32), stacked_features))
    stacked_features = np.column_stack((stacked_features, np.ones(num_samples, dtype=np.float32)))
    stacked_features = np.column_stack((stacked_features, np.ones(num_samples, dtype=np.float32)))
    # append some rows
    stacked_features = np.concatenate((np.ones(9, dtype=np.float32).reshape((1, 9)), stacked_features), axis=0)
    stacked_features = np.concatenate((np.ones(9, dtype=np.float32).reshape((1, 9)), stacked_features), axis=0)
    stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0)
    stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0)
    # test sliced 2d matrix
    sliced_features = stacked_features[2:102, 2:7]
    assert np.all(sliced_features == features)
    sliced_pred = train_and_get_predictions(sliced_features, sliced_labels)
    np.testing.assert_allclose(origin_pred, sliced_pred)
    # test sliced CSR
    stacked_csr = csr_matrix(stacked_features)
    sliced_csr = stacked_csr[2:102, 2:7]
    assert np.all(sliced_csr == features)
    sliced_pred = train_and_get_predictions(sliced_csr, sliced_labels)
    np.testing.assert_allclose(origin_pred, sliced_pred)


def test_init_with_subset():
    data = np.random.random((50, 2))
    y = [1] * 25 + [0] * 25
    lgb_train = lgb.Dataset(data, y, free_raw_data=False)
    subset_index_1 = np.random.choice(np.arange(50), 30, replace=False)
    subset_data_1 = lgb_train.subset(subset_index_1)
    subset_index_2 = np.random.choice(np.arange(50), 20, replace=False)
    subset_data_2 = lgb_train.subset(subset_index_2)
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    params = {"objective": "binary", "verbose": -1}
    init_gbm = lgb.train(params=params, train_set=subset_data_1, num_boost_round=10, keep_training_booster=True)
    lgb.train(params=params, train_set=subset_data_2, num_boost_round=10, init_model=init_gbm)
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    assert lgb_train.get_data().shape[0] == 50
    assert subset_data_1.get_data().shape[0] == 30
    assert subset_data_2.get_data().shape[0] == 20
    lgb_train.save_binary("lgb_train_data.bin")
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    lgb_train_from_file = lgb.Dataset("lgb_train_data.bin", free_raw_data=False)
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    subset_data_3 = lgb_train_from_file.subset(subset_index_1)
    subset_data_4 = lgb_train_from_file.subset(subset_index_2)
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    init_gbm_2 = lgb.train(params=params, train_set=subset_data_3, num_boost_round=10, keep_training_booster=True)
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    with np.testing.assert_raises_regex(lgb.basic.LightGBMError, "Unknown format of training data"):
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        lgb.train(params=params, train_set=subset_data_4, num_boost_round=10, init_model=init_gbm_2)
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    assert lgb_train_from_file.get_data() == "lgb_train_data.bin"
    assert subset_data_3.get_data() == "lgb_train_data.bin"
    assert subset_data_4.get_data() == "lgb_train_data.bin"


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def test_training_on_constructed_subset_without_params():
    X = np.random.random((100, 10))
    y = np.random.random(100)
    lgb_data = lgb.Dataset(X, y)
    subset_indices = [1, 2, 3, 4]
    subset = lgb_data.subset(subset_indices).construct()
    bst = lgb.train({}, subset, num_boost_round=1)
    assert subset.get_params() == {}
    assert subset.num_data() == len(subset_indices)
    assert bst.current_iteration() == 1


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def generate_trainset_for_monotone_constraints_tests(x3_to_category=True):
    number_of_dpoints = 3000
    x1_positively_correlated_with_y = np.random.random(size=number_of_dpoints)
    x2_negatively_correlated_with_y = np.random.random(size=number_of_dpoints)
    x3_negatively_correlated_with_y = np.random.random(size=number_of_dpoints)
    x = np.column_stack(
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        (
            x1_positively_correlated_with_y,
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            x2_negatively_correlated_with_y,
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            categorize(x3_negatively_correlated_with_y) if x3_to_category else x3_negatively_correlated_with_y,
        )
    )
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    zs = np.random.normal(loc=0.0, scale=0.01, size=number_of_dpoints)
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    scales = 10.0 * (np.random.random(6) + 0.5)
    y = (
        scales[0] * x1_positively_correlated_with_y
        + np.sin(scales[1] * np.pi * x1_positively_correlated_with_y)
        - scales[2] * x2_negatively_correlated_with_y
        - np.cos(scales[3] * np.pi * x2_negatively_correlated_with_y)
        - scales[4] * x3_negatively_correlated_with_y
        - np.cos(scales[5] * np.pi * x3_negatively_correlated_with_y)
        + zs
    )
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    categorical_features = []
    if x3_to_category:
        categorical_features = [2]
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    return lgb.Dataset(x, label=y, categorical_feature=categorical_features, free_raw_data=False)
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@pytest.mark.skipif(getenv("TASK", "") == "cuda", reason="Monotone constraints are not yet supported by CUDA version")
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@pytest.mark.parametrize("test_with_categorical_variable", [True, False])
def test_monotone_constraints(test_with_categorical_variable):
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    def is_increasing(y):
        return (np.diff(y) >= 0.0).all()

    def is_decreasing(y):
        return (np.diff(y) <= 0.0).all()

    def is_non_monotone(y):
        return (np.diff(y) < 0.0).any() and (np.diff(y) > 0.0).any()

    def is_correctly_constrained(learner, x3_to_category=True):
        iterations = 10
        n = 1000
        variable_x = np.linspace(0, 1, n).reshape((n, 1))
        fixed_xs_values = np.linspace(0, 1, n)
        for i in range(iterations):
            fixed_x = fixed_xs_values[i] * np.ones((n, 1))
            monotonically_increasing_x = np.column_stack((variable_x, fixed_x, fixed_x))
            monotonically_increasing_y = learner.predict(monotonically_increasing_x)
            monotonically_decreasing_x = np.column_stack((fixed_x, variable_x, fixed_x))
            monotonically_decreasing_y = learner.predict(monotonically_decreasing_x)
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            non_monotone_x = np.column_stack(
                (
                    fixed_x,
                    fixed_x,
                    categorize(variable_x) if x3_to_category else variable_x,
                )
            )
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            non_monotone_y = learner.predict(non_monotone_x)
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            if not (
                is_increasing(monotonically_increasing_y)
                and is_decreasing(monotonically_decreasing_y)
                and is_non_monotone(non_monotone_y)
            ):
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                return False
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        return True
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    def are_interactions_enforced(gbm, feature_sets):
        def parse_tree_features(gbm):
            # trees start at position 1.
            tree_str = gbm.model_to_string().split("Tree")[1:]
            feature_sets = []
            for tree in tree_str:
                # split_features are in 4th line.
                features = tree.splitlines()[3].split("=")[1].split(" ")
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                features = {f"Column_{f}" for f in features}
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                feature_sets.append(features)
            return np.array(feature_sets)

        def has_interaction(treef):
            n = 0
            for fs in feature_sets:
                if len(treef.intersection(fs)) > 0:
                    n += 1
            return n > 1

        tree_features = parse_tree_features(gbm)
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        has_interaction_flag = np.array([has_interaction(treef) for treef in tree_features])
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        return not has_interaction_flag.any()

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    trainset = generate_trainset_for_monotone_constraints_tests(test_with_categorical_variable)
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    for test_with_interaction_constraints in [True, False]:
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        error_msg = (
            "Model not correctly constrained "
            f"(test_with_interaction_constraints={test_with_interaction_constraints})"
        )
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        for monotone_constraints_method in ["basic", "intermediate", "advanced"]:
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            params = {
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                "min_data": 20,
                "num_leaves": 20,
                "monotone_constraints": [1, -1, 0],
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                "monotone_constraints_method": monotone_constraints_method,
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                "use_missing": False,
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            }
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            if test_with_interaction_constraints:
                params["interaction_constraints"] = [[0], [1], [2]]
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            constrained_model = lgb.train(params, trainset)
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            assert is_correctly_constrained(constrained_model, test_with_categorical_variable), error_msg
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            if test_with_interaction_constraints:
                feature_sets = [["Column_0"], ["Column_1"], "Column_2"]
                assert are_interactions_enforced(constrained_model, feature_sets)
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@pytest.mark.skipif(getenv("TASK", "") == "cuda", reason="Monotone constraints are not yet supported by CUDA version")
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def test_monotone_penalty():
    def are_first_splits_non_monotone(tree, n, monotone_constraints):
        if n <= 0:
            return True
        if "leaf_value" in tree:
            return True
        if monotone_constraints[tree["split_feature"]] != 0:
            return False
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        return are_first_splits_non_monotone(
            tree["left_child"], n - 1, monotone_constraints
        ) and are_first_splits_non_monotone(tree["right_child"], n - 1, monotone_constraints)
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    def are_there_monotone_splits(tree, monotone_constraints):
        if "leaf_value" in tree:
            return False
        if monotone_constraints[tree["split_feature"]] != 0:
            return True
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        return are_there_monotone_splits(tree["left_child"], monotone_constraints) or are_there_monotone_splits(
            tree["right_child"], monotone_constraints
        )
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    max_depth = 5
    monotone_constraints = [1, -1, 0]
    penalization_parameter = 2.0
    trainset = generate_trainset_for_monotone_constraints_tests(x3_to_category=False)
    for monotone_constraints_method in ["basic", "intermediate", "advanced"]:
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        params = {
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            "max_depth": max_depth,
            "monotone_constraints": monotone_constraints,
            "monotone_penalty": penalization_parameter,
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            "monotone_constraints_method": monotone_constraints_method,
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        }
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        constrained_model = lgb.train(params, trainset, 10)
        dumped_model = constrained_model.dump_model()["tree_info"]
        for tree in dumped_model:
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            assert are_first_splits_non_monotone(
                tree["tree_structure"], int(penalization_parameter), monotone_constraints
            )
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            assert are_there_monotone_splits(tree["tree_structure"], monotone_constraints)


# test if a penalty as high as the depth indeed prohibits all monotone splits
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@pytest.mark.skipif(getenv("TASK", "") == "cuda", reason="Monotone constraints are not yet supported by CUDA version")
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def test_monotone_penalty_max():
    max_depth = 5
    monotone_constraints = [1, -1, 0]
    penalization_parameter = max_depth
    trainset_constrained_model = generate_trainset_for_monotone_constraints_tests(x3_to_category=False)
    x = trainset_constrained_model.data
    y = trainset_constrained_model.label
    x3_negatively_correlated_with_y = x[:, 2]
    trainset_unconstrained_model = lgb.Dataset(x3_negatively_correlated_with_y.reshape(-1, 1), label=y)
    params_constrained_model = {
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        "monotone_constraints": monotone_constraints,
        "monotone_penalty": penalization_parameter,
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        "max_depth": max_depth,
        "gpu_use_dp": True,
    }
    params_unconstrained_model = {
        "max_depth": max_depth,
        "gpu_use_dp": True,
    }

    unconstrained_model = lgb.train(params_unconstrained_model, trainset_unconstrained_model, 10)
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    unconstrained_model_predictions = unconstrained_model.predict(x3_negatively_correlated_with_y.reshape(-1, 1))
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    for monotone_constraints_method in ["basic", "intermediate", "advanced"]:
        params_constrained_model["monotone_constraints_method"] = monotone_constraints_method
        # The penalization is so high that the first 2 features should not be used here
        constrained_model = lgb.train(params_constrained_model, trainset_constrained_model, 10)

        # Check that a very high penalization is the same as not using the features at all
        np.testing.assert_array_equal(constrained_model.predict(x), unconstrained_model_predictions)


def test_max_bin_by_feature():
    col1 = np.arange(0, 100)[:, np.newaxis]
    col2 = np.zeros((100, 1))
    col2[20:] = 1
    X = np.concatenate([col1, col2], axis=1)
    y = np.arange(0, 100)
    params = {
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        "objective": "regression_l2",
        "verbose": -1,
        "num_leaves": 100,
        "min_data_in_leaf": 1,
        "min_sum_hessian_in_leaf": 0,
        "min_data_in_bin": 1,
        "max_bin_by_feature": [100, 2],
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    }
    lgb_data = lgb.Dataset(X, label=y)
    est = lgb.train(params, lgb_data, num_boost_round=1)
    assert len(np.unique(est.predict(X))) == 100
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    params["max_bin_by_feature"] = [2, 100]
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    lgb_data = lgb.Dataset(X, label=y)
    est = lgb.train(params, lgb_data, num_boost_round=1)
    assert len(np.unique(est.predict(X))) == 3


def test_small_max_bin():
    np.random.seed(0)
    y = np.random.choice([0, 1], 100)
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    x = np.ones((100, 1))
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    x[:30, 0] = -1
    x[60:, 0] = 2
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    params = {"objective": "binary", "seed": 0, "min_data_in_leaf": 1, "verbose": -1, "max_bin": 2}
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    lgb_x = lgb.Dataset(x, label=y)
    lgb.train(params, lgb_x, num_boost_round=5)
    x[0, 0] = np.nan
2163
    params["max_bin"] = 3
2164
2165
2166
2167
2168
2169
2170
2171
    lgb_x = lgb.Dataset(x, label=y)
    lgb.train(params, lgb_x, num_boost_round=5)
    np.random.seed()  # reset seed


def test_refit():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
2172
    params = {"objective": "binary", "metric": "binary_logloss", "verbose": -1, "min_data": 10}
2173
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2176
2177
2178
2179
2180
    lgb_train = lgb.Dataset(X_train, y_train)
    gbm = lgb.train(params, lgb_train, num_boost_round=20)
    err_pred = log_loss(y_test, gbm.predict(X_test))
    new_gbm = gbm.refit(X_test, y_test)
    new_err_pred = log_loss(y_test, new_gbm.predict(X_test))
    assert err_pred > new_err_pred


2181
2182
2183
2184
def test_refit_dataset_params():
    # check refit accepts dataset_params
    X, y = load_breast_cancer(return_X_y=True)
    lgb_train = lgb.Dataset(X, y, init_score=np.zeros(y.size))
2185
    train_params = {"objective": "binary", "verbose": -1, "seed": 123}
2186
2187
2188
2189
    gbm = lgb.train(train_params, lgb_train, num_boost_round=10)
    non_weight_err_pred = log_loss(y, gbm.predict(X))
    refit_weight = np.random.rand(y.shape[0])
    dataset_params = {
2190
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2192
        "max_bin": 260,
        "min_data_in_bin": 5,
        "data_random_seed": 123,
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
    }
    new_gbm = gbm.refit(
        data=X,
        label=y,
        weight=refit_weight,
        dataset_params=dataset_params,
        decay_rate=0.0,
    )
    weight_err_pred = log_loss(y, new_gbm.predict(X))
    train_set_params = new_gbm.train_set.get_params()
    stored_weights = new_gbm.train_set.get_weight()
    assert weight_err_pred != non_weight_err_pred
    assert train_set_params["max_bin"] == 260
    assert train_set_params["min_data_in_bin"] == 5
    assert train_set_params["data_random_seed"] == 123
    np.testing.assert_allclose(stored_weights, refit_weight)


2211
@pytest.mark.parametrize("boosting_type", ["rf", "dart"])
2212
2213
2214
def test_mape_for_specific_boosting_types(boosting_type):
    X, y = make_synthetic_regression()
    y = abs(y)
2215
    params = {
2216
2217
2218
2219
2220
2221
2222
        "boosting_type": boosting_type,
        "objective": "mape",
        "verbose": -1,
        "bagging_freq": 1,
        "bagging_fraction": 0.8,
        "feature_fraction": 0.8,
        "boost_from_average": True,
2223
2224
2225
2226
2227
    }
    lgb_train = lgb.Dataset(X, y)
    gbm = lgb.train(params, lgb_train, num_boost_round=20)
    pred = gbm.predict(X)
    pred_mean = pred.mean()
2228
2229
2230
    # the following checks that dart and rf with mape can predict outside the 0-1 range
    # https://github.com/microsoft/LightGBM/issues/1579
    assert pred_mean > 8
2231
2232
2233
2234
2235
2236


def check_constant_features(y_true, expected_pred, more_params):
    X_train = np.ones((len(y_true), 1))
    y_train = np.array(y_true)
    params = {
2237
2238
2239
2240
2241
2242
2243
2244
        "objective": "regression",
        "num_class": 1,
        "verbose": -1,
        "min_data": 1,
        "num_leaves": 2,
        "learning_rate": 1,
        "min_data_in_bin": 1,
        "boost_from_average": True,
2245
2246
2247
2248
2249
2250
2251
2252
2253
    }
    params.update(more_params)
    lgb_train = lgb.Dataset(X_train, y_train, params=params)
    gbm = lgb.train(params, lgb_train, num_boost_round=2)
    pred = gbm.predict(X_train)
    assert np.allclose(pred, expected_pred)


def test_constant_features_regression():
2254
    params = {"objective": "regression"}
2255
2256
2257
2258
2259
2260
    check_constant_features([0.0, 10.0, 0.0, 10.0], 5.0, params)
    check_constant_features([0.0, 1.0, 2.0, 3.0], 1.5, params)
    check_constant_features([-1.0, 1.0, -2.0, 2.0], 0.0, params)


def test_constant_features_binary():
2261
    params = {"objective": "binary"}
2262
2263
2264
2265
2266
    check_constant_features([0.0, 10.0, 0.0, 10.0], 0.5, params)
    check_constant_features([0.0, 1.0, 2.0, 3.0], 0.75, params)


def test_constant_features_multiclass():
2267
    params = {"objective": "multiclass", "num_class": 3}
2268
2269
2270
2271
2272
    check_constant_features([0.0, 1.0, 2.0, 0.0], [0.5, 0.25, 0.25], params)
    check_constant_features([0.0, 1.0, 2.0, 1.0], [0.25, 0.5, 0.25], params)


def test_constant_features_multiclassova():
2273
    params = {"objective": "multiclassova", "num_class": 3}
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
    check_constant_features([0.0, 1.0, 2.0, 0.0], [0.5, 0.25, 0.25], params)
    check_constant_features([0.0, 1.0, 2.0, 1.0], [0.25, 0.5, 0.25], params)


def test_fpreproc():
    def preprocess_data(dtrain, dtest, params):
        train_data = dtrain.construct().get_data()
        test_data = dtest.construct().get_data()
        train_data[:, 0] += 1
        test_data[:, 0] += 1
        dtrain.label[-5:] = 3
        dtest.label[-5:] = 3
        dtrain = lgb.Dataset(train_data, dtrain.label)
        dtest = lgb.Dataset(test_data, dtest.label, reference=dtrain)
2288
        params["num_class"] = 4
2289
2290
2291
2292
        return dtrain, dtest, params

    X, y = load_iris(return_X_y=True)
    dataset = lgb.Dataset(X, y, free_raw_data=False)
2293
    params = {"objective": "multiclass", "num_class": 3, "verbose": -1}
2294
    results = lgb.cv(params, dataset, num_boost_round=10, fpreproc=preprocess_data)
2295
2296
    assert "valid multi_logloss-mean" in results
    assert len(results["valid multi_logloss-mean"]) == 10
2297
2298
2299
2300
2301


def test_metrics():
    X, y = load_digits(n_class=2, return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
2302
2303
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_valid = lgb.Dataset(X_test, y_test, reference=lgb_train)
2304
2305

    evals_result = {}
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
    params_dummy_obj_verbose = {"verbose": -1, "objective": dummy_obj}
    params_obj_verbose = {"objective": "binary", "verbose": -1}
    params_obj_metric_log_verbose = {"objective": "binary", "metric": "binary_logloss", "verbose": -1}
    params_obj_metric_err_verbose = {"objective": "binary", "metric": "binary_error", "verbose": -1}
    params_obj_metric_inv_verbose = {"objective": "binary", "metric": "invalid_metric", "verbose": -1}
    params_obj_metric_quant_verbose = {"objective": "regression", "metric": "quantile", "verbose": 2}
    params_obj_metric_multi_verbose = {
        "objective": "binary",
        "metric": ["binary_logloss", "binary_error"],
        "verbose": -1,
    }
    params_obj_metric_none_verbose = {"objective": "binary", "metric": "None", "verbose": -1}
    params_dummy_obj_metric_log_verbose = {"objective": dummy_obj, "metric": "binary_logloss", "verbose": -1}
    params_dummy_obj_metric_err_verbose = {"objective": dummy_obj, "metric": "binary_error", "verbose": -1}
    params_dummy_obj_metric_inv_verbose = {"objective": dummy_obj, "metric_types": "invalid_metric", "verbose": -1}
    params_dummy_obj_metric_multi_verbose = {
        "objective": dummy_obj,
        "metric": ["binary_logloss", "binary_error"],
        "verbose": -1,
    }
    params_dummy_obj_metric_none_verbose = {"objective": dummy_obj, "metric": "None", "verbose": -1}
2327
2328

    def get_cv_result(params=params_obj_verbose, **kwargs):
2329
        return lgb.cv(params, lgb_train, num_boost_round=2, **kwargs)
2330
2331

    def train_booster(params=params_obj_verbose, **kwargs):
2332
2333
2334
2335
2336
2337
        lgb.train(
            params,
            lgb_train,
            num_boost_round=2,
            valid_sets=[lgb_valid],
            callbacks=[lgb.record_evaluation(evals_result)],
2338
            **kwargs,
2339
        )
2340

2341
    # no custom objective, no feval
2342
2343
2344
    # default metric
    res = get_cv_result()
    assert len(res) == 2
2345
    assert "valid binary_logloss-mean" in res
2346
2347
2348
2349

    # non-default metric in params
    res = get_cv_result(params=params_obj_metric_err_verbose)
    assert len(res) == 2
2350
    assert "valid binary_error-mean" in res
2351
2352

    # default metric in args
2353
    res = get_cv_result(metrics="binary_logloss")
2354
    assert len(res) == 2
2355
    assert "valid binary_logloss-mean" in res
2356
2357

    # non-default metric in args
2358
    res = get_cv_result(metrics="binary_error")
2359
    assert len(res) == 2
2360
    assert "valid binary_error-mean" in res
2361
2362

    # metric in args overwrites one in params
2363
    res = get_cv_result(params=params_obj_metric_inv_verbose, metrics="binary_error")
2364
    assert len(res) == 2
2365
    assert "valid binary_error-mean" in res
2366

2367
2368
2369
    # metric in args overwrites one in params
    res = get_cv_result(params=params_obj_metric_quant_verbose)
    assert len(res) == 2
2370
    assert "valid quantile-mean" in res
2371

2372
2373
2374
    # multiple metrics in params
    res = get_cv_result(params=params_obj_metric_multi_verbose)
    assert len(res) == 4
2375
2376
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
2377
2378

    # multiple metrics in args
2379
    res = get_cv_result(metrics=["binary_logloss", "binary_error"])
2380
    assert len(res) == 4
2381
2382
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
2383
2384

    # remove default metric by 'None' in list
2385
    res = get_cv_result(metrics=["None"])
2386
2387
2388
    assert len(res) == 0

    # remove default metric by 'None' aliases
2389
    for na_alias in ("None", "na", "null", "custom"):
2390
2391
2392
        res = get_cv_result(metrics=na_alias)
        assert len(res) == 0

2393
    # custom objective, no feval
2394
    # no default metric
2395
    res = get_cv_result(params=params_dummy_obj_verbose)
2396
2397
2398
    assert len(res) == 0

    # metric in params
2399
    res = get_cv_result(params=params_dummy_obj_metric_err_verbose)
2400
    assert len(res) == 2
2401
    assert "valid binary_error-mean" in res
2402
2403

    # metric in args
2404
    res = get_cv_result(params=params_dummy_obj_verbose, metrics="binary_error")
2405
    assert len(res) == 2
2406
    assert "valid binary_error-mean" in res
2407
2408

    # metric in args overwrites its' alias in params
2409
    res = get_cv_result(params=params_dummy_obj_metric_inv_verbose, metrics="binary_error")
2410
    assert len(res) == 2
2411
    assert "valid binary_error-mean" in res
2412
2413

    # multiple metrics in params
2414
    res = get_cv_result(params=params_dummy_obj_metric_multi_verbose)
2415
    assert len(res) == 4
2416
2417
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
2418
2419

    # multiple metrics in args
2420
    res = get_cv_result(params=params_dummy_obj_verbose, metrics=["binary_logloss", "binary_error"])
2421
    assert len(res) == 4
2422
2423
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
2424

2425
    # no custom objective, feval
2426
2427
2428
    # default metric with custom one
    res = get_cv_result(feval=constant_metric)
    assert len(res) == 4
2429
2430
    assert "valid binary_logloss-mean" in res
    assert "valid error-mean" in res
2431
2432
2433
2434

    # non-default metric in params with custom one
    res = get_cv_result(params=params_obj_metric_err_verbose, feval=constant_metric)
    assert len(res) == 4
2435
2436
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2437
2438

    # default metric in args with custom one
2439
    res = get_cv_result(metrics="binary_logloss", feval=constant_metric)
2440
    assert len(res) == 4
2441
2442
    assert "valid binary_logloss-mean" in res
    assert "valid error-mean" in res
2443
2444

    # non-default metric in args with custom one
2445
    res = get_cv_result(metrics="binary_error", feval=constant_metric)
2446
    assert len(res) == 4
2447
2448
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2449
2450

    # metric in args overwrites one in params, custom one is evaluated too
2451
    res = get_cv_result(params=params_obj_metric_inv_verbose, metrics="binary_error", feval=constant_metric)
2452
    assert len(res) == 4
2453
2454
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2455
2456
2457
2458

    # multiple metrics in params with custom one
    res = get_cv_result(params=params_obj_metric_multi_verbose, feval=constant_metric)
    assert len(res) == 6
2459
2460
2461
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2462
2463

    # multiple metrics in args with custom one
2464
    res = get_cv_result(metrics=["binary_logloss", "binary_error"], feval=constant_metric)
2465
    assert len(res) == 6
2466
2467
2468
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2469
2470

    # custom metric is evaluated despite 'None' is passed
2471
    res = get_cv_result(metrics=["None"], feval=constant_metric)
2472
    assert len(res) == 2
2473
    assert "valid error-mean" in res
2474

2475
    # custom objective, feval
2476
    # no default metric, only custom one
2477
    res = get_cv_result(params=params_dummy_obj_verbose, feval=constant_metric)
2478
    assert len(res) == 2
2479
    assert "valid error-mean" in res
2480
2481

    # metric in params with custom one
2482
    res = get_cv_result(params=params_dummy_obj_metric_err_verbose, feval=constant_metric)
2483
    assert len(res) == 4
2484
2485
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2486
2487

    # metric in args with custom one
2488
    res = get_cv_result(params=params_dummy_obj_verbose, feval=constant_metric, metrics="binary_error")
2489
    assert len(res) == 4
2490
2491
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2492
2493

    # metric in args overwrites one in params, custom one is evaluated too
2494
    res = get_cv_result(params=params_dummy_obj_metric_inv_verbose, feval=constant_metric, metrics="binary_error")
2495
    assert len(res) == 4
2496
2497
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2498
2499

    # multiple metrics in params with custom one
2500
    res = get_cv_result(params=params_dummy_obj_metric_multi_verbose, feval=constant_metric)
2501
    assert len(res) == 6
2502
2503
2504
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2505
2506

    # multiple metrics in args with custom one
2507
2508
2509
    res = get_cv_result(
        params=params_dummy_obj_verbose, feval=constant_metric, metrics=["binary_logloss", "binary_error"]
    )
2510
    assert len(res) == 6
2511
2512
2513
    assert "valid binary_logloss-mean" in res
    assert "valid binary_error-mean" in res
    assert "valid error-mean" in res
2514
2515

    # custom metric is evaluated despite 'None' is passed
2516
    res = get_cv_result(params=params_dummy_obj_metric_none_verbose, feval=constant_metric)
2517
    assert len(res) == 2
2518
    assert "valid error-mean" in res
2519

2520
    # no custom objective, no feval
2521
2522
    # default metric
    train_booster()
2523
2524
    assert len(evals_result["valid_0"]) == 1
    assert "binary_logloss" in evals_result["valid_0"]
2525
2526
2527

    # default metric in params
    train_booster(params=params_obj_metric_log_verbose)
2528
2529
    assert len(evals_result["valid_0"]) == 1
    assert "binary_logloss" in evals_result["valid_0"]
2530
2531
2532

    # non-default metric in params
    train_booster(params=params_obj_metric_err_verbose)
2533
2534
    assert len(evals_result["valid_0"]) == 1
    assert "binary_error" in evals_result["valid_0"]
2535
2536
2537

    # multiple metrics in params
    train_booster(params=params_obj_metric_multi_verbose)
2538
2539
2540
    assert len(evals_result["valid_0"]) == 2
    assert "binary_logloss" in evals_result["valid_0"]
    assert "binary_error" in evals_result["valid_0"]
2541
2542

    # remove default metric by 'None' aliases
2543
2544
    for na_alias in ("None", "na", "null", "custom"):
        params = {"objective": "binary", "metric": na_alias, "verbose": -1}
2545
2546
2547
        train_booster(params=params)
        assert len(evals_result) == 0

2548
    # custom objective, no feval
2549
    # no default metric
2550
    train_booster(params=params_dummy_obj_verbose)
2551
2552
2553
    assert len(evals_result) == 0

    # metric in params
2554
    train_booster(params=params_dummy_obj_metric_log_verbose)
2555
2556
    assert len(evals_result["valid_0"]) == 1
    assert "binary_logloss" in evals_result["valid_0"]
2557
2558

    # multiple metrics in params
2559
    train_booster(params=params_dummy_obj_metric_multi_verbose)
2560
2561
2562
    assert len(evals_result["valid_0"]) == 2
    assert "binary_logloss" in evals_result["valid_0"]
    assert "binary_error" in evals_result["valid_0"]
2563

2564
    # no custom objective, feval
2565
2566
    # default metric with custom one
    train_booster(feval=constant_metric)
2567
2568
2569
    assert len(evals_result["valid_0"]) == 2
    assert "binary_logloss" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2570
2571
2572

    # default metric in params with custom one
    train_booster(params=params_obj_metric_log_verbose, feval=constant_metric)
2573
2574
2575
    assert len(evals_result["valid_0"]) == 2
    assert "binary_logloss" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2576
2577
2578

    # non-default metric in params with custom one
    train_booster(params=params_obj_metric_err_verbose, feval=constant_metric)
2579
2580
2581
    assert len(evals_result["valid_0"]) == 2
    assert "binary_error" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2582
2583
2584

    # multiple metrics in params with custom one
    train_booster(params=params_obj_metric_multi_verbose, feval=constant_metric)
2585
2586
2587
2588
    assert len(evals_result["valid_0"]) == 3
    assert "binary_logloss" in evals_result["valid_0"]
    assert "binary_error" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2589
2590
2591
2592

    # custom metric is evaluated despite 'None' is passed
    train_booster(params=params_obj_metric_none_verbose, feval=constant_metric)
    assert len(evals_result) == 1
2593
    assert "error" in evals_result["valid_0"]
2594

2595
    # custom objective, feval
2596
    # no default metric, only custom one
2597
    train_booster(params=params_dummy_obj_verbose, feval=constant_metric)
2598
2599
    assert len(evals_result["valid_0"]) == 1
    assert "error" in evals_result["valid_0"]
2600
2601

    # metric in params with custom one
2602
    train_booster(params=params_dummy_obj_metric_log_verbose, feval=constant_metric)
2603
2604
2605
    assert len(evals_result["valid_0"]) == 2
    assert "binary_logloss" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2606
2607

    # multiple metrics in params with custom one
2608
    train_booster(params=params_dummy_obj_metric_multi_verbose, feval=constant_metric)
2609
2610
2611
2612
    assert len(evals_result["valid_0"]) == 3
    assert "binary_logloss" in evals_result["valid_0"]
    assert "binary_error" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
2613
2614

    # custom metric is evaluated despite 'None' is passed
2615
    train_booster(params=params_dummy_obj_metric_none_verbose, feval=constant_metric)
2616
    assert len(evals_result) == 1
2617
    assert "error" in evals_result["valid_0"]
2618
2619

    X, y = load_digits(n_class=3, return_X_y=True)
2620
    lgb_train = lgb.Dataset(X, y)
2621

2622
    obj_multi_aliases = ["multiclass", "softmax", "multiclassova", "multiclass_ova", "ova", "ovr"]
2623
    for obj_multi_alias in obj_multi_aliases:
2624
        # Custom objective replaces multiclass
2625
2626
2627
2628
2629
        params_obj_class_3_verbose = {"objective": obj_multi_alias, "num_class": 3, "verbose": -1}
        params_dummy_obj_class_3_verbose = {"objective": dummy_obj, "num_class": 3, "verbose": -1}
        params_dummy_obj_class_1_verbose = {"objective": dummy_obj, "num_class": 1, "verbose": -1}
        params_obj_verbose = {"objective": obj_multi_alias, "verbose": -1}
        params_dummy_obj_verbose = {"objective": dummy_obj, "verbose": -1}
2630
2631
2632
        # multiclass default metric
        res = get_cv_result(params_obj_class_3_verbose)
        assert len(res) == 2
2633
        assert "valid multi_logloss-mean" in res
2634
2635
2636
        # multiclass default metric with custom one
        res = get_cv_result(params_obj_class_3_verbose, feval=constant_metric)
        assert len(res) == 4
2637
2638
        assert "valid multi_logloss-mean" in res
        assert "valid error-mean" in res
2639
        # multiclass metric alias with custom one for custom objective
2640
        res = get_cv_result(params_dummy_obj_class_3_verbose, feval=constant_metric)
2641
        assert len(res) == 2
2642
        assert "valid error-mean" in res
2643
        # no metric for invalid class_num
2644
        res = get_cv_result(params_dummy_obj_class_1_verbose)
2645
2646
        assert len(res) == 0
        # custom metric for invalid class_num
2647
        res = get_cv_result(params_dummy_obj_class_1_verbose, feval=constant_metric)
2648
        assert len(res) == 2
2649
        assert "valid error-mean" in res
2650
2651
        # multiclass metric alias with custom one with invalid class_num
        with pytest.raises(lgb.basic.LightGBMError):
2652
            get_cv_result(params_dummy_obj_class_1_verbose, metrics=obj_multi_alias, feval=constant_metric)
2653
2654
2655
        # multiclass default metric without num_class
        with pytest.raises(lgb.basic.LightGBMError):
            get_cv_result(params_obj_verbose)
2656
        for metric_multi_alias in obj_multi_aliases + ["multi_logloss"]:
2657
2658
2659
            # multiclass metric alias
            res = get_cv_result(params_obj_class_3_verbose, metrics=metric_multi_alias)
            assert len(res) == 2
2660
            assert "valid multi_logloss-mean" in res
2661
        # multiclass metric
2662
        res = get_cv_result(params_obj_class_3_verbose, metrics="multi_error")
2663
        assert len(res) == 2
2664
        assert "valid multi_error-mean" in res
2665
2666
        # non-valid metric for multiclass objective
        with pytest.raises(lgb.basic.LightGBMError):
2667
2668
            get_cv_result(params_obj_class_3_verbose, metrics="binary_logloss")
    params_class_3_verbose = {"num_class": 3, "verbose": -1}
2669
2670
2671
2672
    # non-default num_class for default objective
    with pytest.raises(lgb.basic.LightGBMError):
        get_cv_result(params_class_3_verbose)
    # no metric with non-default num_class for custom objective
2673
    res = get_cv_result(params_dummy_obj_class_3_verbose)
2674
    assert len(res) == 0
2675
    for metric_multi_alias in obj_multi_aliases + ["multi_logloss"]:
2676
        # multiclass metric alias for custom objective
2677
        res = get_cv_result(params_dummy_obj_class_3_verbose, metrics=metric_multi_alias)
2678
        assert len(res) == 2
2679
        assert "valid multi_logloss-mean" in res
2680
    # multiclass metric for custom objective
2681
    res = get_cv_result(params_dummy_obj_class_3_verbose, metrics="multi_error")
2682
    assert len(res) == 2
2683
    assert "valid multi_error-mean" in res
2684
2685
    # binary metric with non-default num_class for custom objective
    with pytest.raises(lgb.basic.LightGBMError):
2686
        get_cv_result(params_dummy_obj_class_3_verbose, metrics="binary_error")
2687
2688
2689
2690
2691


def test_multiple_feval_train():
    X, y = load_breast_cancer(return_X_y=True)

2692
    params = {"verbose": -1, "objective": "binary", "metric": "binary_logloss"}
2693
2694
2695

    X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.2)

2696
2697
    train_dataset = lgb.Dataset(data=X_train, label=y_train)
    validation_dataset = lgb.Dataset(data=X_validation, label=y_validation, reference=train_dataset)
2698
2699
2700
2701
2702
2703
2704
    evals_result = {}
    lgb.train(
        params=params,
        train_set=train_dataset,
        valid_sets=validation_dataset,
        num_boost_round=5,
        feval=[constant_metric, decreasing_metric],
2705
        callbacks=[lgb.record_evaluation(evals_result)],
2706
    )
2707

2708
2709
2710
2711
    assert len(evals_result["valid_0"]) == 3
    assert "binary_logloss" in evals_result["valid_0"]
    assert "error" in evals_result["valid_0"]
    assert "decreasing_metric" in evals_result["valid_0"]
2712
2713


2714
2715
def test_objective_callable_train_binary_classification():
    X, y = load_breast_cancer(return_X_y=True)
2716
    params = {"verbose": -1, "objective": logloss_obj, "learning_rate": 0.01}
2717
    train_dataset = lgb.Dataset(X, y)
2718
    booster = lgb.train(params=params, train_set=train_dataset, num_boost_round=20)
2719
2720
2721
    y_pred = logistic_sigmoid(booster.predict(X))
    logloss_error = log_loss(y, y_pred)
    rocauc_error = roc_auc_score(y, y_pred)
2722
    assert booster.params["objective"] == "none"
2723
2724
    assert logloss_error == pytest.approx(0.547907)
    assert rocauc_error == pytest.approx(0.995944)
2725
2726
2727
2728


def test_objective_callable_train_regression():
    X, y = make_synthetic_regression()
2729
    params = {"verbose": -1, "objective": mse_obj}
2730
    lgb_train = lgb.Dataset(X, y)
2731
    booster = lgb.train(params, lgb_train, num_boost_round=20)
2732
2733
    y_pred = booster.predict(X)
    mse_error = mean_squared_error(y, y_pred)
2734
    assert booster.params["objective"] == "none"
2735
    assert mse_error == pytest.approx(286.724194)
2736
2737
2738
2739


def test_objective_callable_cv_binary_classification():
    X, y = load_breast_cancer(return_X_y=True)
2740
    params = {"verbose": -1, "objective": logloss_obj, "learning_rate": 0.01}
2741
    train_dataset = lgb.Dataset(X, y)
2742
2743
2744
2745
    cv_res = lgb.cv(params, train_dataset, num_boost_round=20, nfold=3, return_cvbooster=True)
    cv_booster = cv_res["cvbooster"].boosters
    cv_logloss_errors = [log_loss(y, logistic_sigmoid(cb.predict(X))) < 0.56 for cb in cv_booster]
    cv_objs = [cb.params["objective"] == "none" for cb in cv_booster]
2746
2747
2748
2749
2750
2751
2752
    assert all(cv_objs)
    assert all(cv_logloss_errors)


def test_objective_callable_cv_regression():
    X, y = make_synthetic_regression()
    lgb_train = lgb.Dataset(X, y)
2753
2754
2755
2756
2757
    params = {"verbose": -1, "objective": mse_obj}
    cv_res = lgb.cv(params, lgb_train, num_boost_round=20, nfold=3, stratified=False, return_cvbooster=True)
    cv_booster = cv_res["cvbooster"].boosters
    cv_mse_errors = [mean_squared_error(y, cb.predict(X)) < 463 for cb in cv_booster]
    cv_objs = [cb.params["objective"] == "none" for cb in cv_booster]
2758
2759
2760
2761
    assert all(cv_objs)
    assert all(cv_mse_errors)


2762
2763
2764
def test_multiple_feval_cv():
    X, y = load_breast_cancer(return_X_y=True)

2765
    params = {"verbose": -1, "objective": "binary", "metric": "binary_logloss"}
2766

2767
    train_dataset = lgb.Dataset(data=X, label=y)
2768
2769

    cv_results = lgb.cv(
2770
2771
        params=params, train_set=train_dataset, num_boost_round=5, feval=[constant_metric, decreasing_metric]
    )
2772
2773
2774

    # Expect three metrics but mean and stdv for each metric
    assert len(cv_results) == 6
2775
2776
2777
2778
2779
2780
    assert "valid binary_logloss-mean" in cv_results
    assert "valid error-mean" in cv_results
    assert "valid decreasing_metric-mean" in cv_results
    assert "valid binary_logloss-stdv" in cv_results
    assert "valid error-stdv" in cv_results
    assert "valid decreasing_metric-stdv" in cv_results
2781
2782


2783
2784
2785
2786
2787
2788
def test_default_objective_and_metric():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
    train_dataset = lgb.Dataset(data=X_train, label=y_train)
    validation_dataset = lgb.Dataset(data=X_test, label=y_test, reference=train_dataset)
    evals_result = {}
2789
    params = {"verbose": -1}
2790
2791
2792
2793
2794
    lgb.train(
        params=params,
        train_set=train_dataset,
        valid_sets=validation_dataset,
        num_boost_round=5,
2795
        callbacks=[lgb.record_evaluation(evals_result)],
2796
2797
    )

2798
2799
2800
2801
    assert "valid_0" in evals_result
    assert len(evals_result["valid_0"]) == 1
    assert "l2" in evals_result["valid_0"]
    assert len(evals_result["valid_0"]["l2"]) == 5
2802
2803


2804
@pytest.mark.parametrize("use_weight", [True, False])
2805
def test_multiclass_custom_objective(use_weight):
2806
2807
    def custom_obj(y_pred, ds):
        y_true = ds.get_label()
2808
2809
2810
        weight = ds.get_weight()
        grad, hess = sklearn_multiclass_custom_objective(y_true, y_pred, weight)
        return grad, hess
2811
2812
2813

    centers = [[-4, -4], [4, 4], [-4, 4]]
    X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
2814
    weight = np.full_like(y, 2)
2815
    ds = lgb.Dataset(X, y)
2816
2817
    if use_weight:
        ds.set_weight(weight)
2818
    params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7}
2819
2820
2821
    builtin_obj_bst = lgb.train(params, ds, num_boost_round=10)
    builtin_obj_preds = builtin_obj_bst.predict(X)

2822
    params["objective"] = custom_obj
2823
    custom_obj_bst = lgb.train(params, ds, num_boost_round=10)
2824
2825
2826
2827
2828
    custom_obj_preds = softmax(custom_obj_bst.predict(X))

    np.testing.assert_allclose(builtin_obj_preds, custom_obj_preds, rtol=0.01)


2829
@pytest.mark.parametrize("use_weight", [True, False])
2830
def test_multiclass_custom_eval(use_weight):
2831
2832
    def custom_eval(y_pred, ds):
        y_true = ds.get_label()
2833
2834
        weight = ds.get_weight()  # weight is None when not set
        loss = log_loss(y_true, y_pred, sample_weight=weight)
2835
        return "custom_logloss", loss, False
2836
2837
2838

    centers = [[-4, -4], [4, 4], [-4, 4]]
    X, y = make_blobs(n_samples=1_000, centers=centers, random_state=42)
2839
2840
2841
2842
    weight = np.full_like(y, 2)
    X_train, X_valid, y_train, y_valid, weight_train, weight_valid = train_test_split(
        X, y, weight, test_size=0.2, random_state=0
    )
2843
2844
    train_ds = lgb.Dataset(X_train, y_train)
    valid_ds = lgb.Dataset(X_valid, y_valid, reference=train_ds)
2845
2846
2847
    if use_weight:
        train_ds.set_weight(weight_train)
        valid_ds.set_weight(weight_valid)
2848
    params = {"objective": "multiclass", "num_class": 3, "num_leaves": 7}
2849
2850
2851
2852
2853
2854
    eval_result = {}
    bst = lgb.train(
        params,
        train_ds,
        num_boost_round=10,
        valid_sets=[train_ds, valid_ds],
2855
        valid_names=["train", "valid"],
2856
2857
2858
2859
2860
        feval=custom_eval,
        callbacks=[lgb.record_evaluation(eval_result)],
        keep_training_booster=True,
    )

2861
2862
    for key, ds in zip(["train", "valid"], [train_ds, valid_ds]):
        np.testing.assert_allclose(eval_result[key]["multi_logloss"], eval_result[key]["custom_logloss"])
2863
        _, metric, value, _ = bst.eval(ds, key, feval=custom_eval)[1]  # first element is multi_logloss
2864
        assert metric == "custom_logloss"
2865
2866
2867
        np.testing.assert_allclose(value, eval_result[key][metric][-1])


2868
@pytest.mark.skipif(psutil.virtual_memory().available / 1024 / 1024 / 1024 < 3, reason="not enough RAM")
2869
def test_model_size():
2870
    X, y = make_synthetic_regression()
2871
    data = lgb.Dataset(X, y)
2872
    bst = lgb.train({"verbose": -1}, data, num_boost_round=2)
2873
2874
    y_pred = bst.predict(X)
    model_str = bst.model_to_string()
2875
    one_tree = model_str[model_str.find("Tree=1") : model_str.find("end of trees")]
2876
    one_tree_size = len(one_tree)
2877
    one_tree = one_tree.replace("Tree=1", "Tree={}")
2878
2879
2880
    multiplier = 100
    total_trees = multiplier + 2
    try:
2881
2882
        before_tree_sizes = model_str[: model_str.find("tree_sizes")]
        trees = model_str[model_str.find("Tree=0") : model_str.find("end of trees")]
2883
        more_trees = (one_tree * multiplier).format(*range(2, total_trees))
2884
        after_trees = model_str[model_str.find("end of trees") :]
2885
2886
        num_end_spaces = 2**31 - one_tree_size * total_trees
        new_model_str = f"{before_tree_sizes}\n\n{trees}{more_trees}{after_trees}{'':{num_end_spaces}}"
2887
        assert len(new_model_str) > 2**31
2888
        bst.model_from_string(new_model_str)
2889
2890
2891
2892
        assert bst.num_trees() == total_trees
        y_pred_new = bst.predict(X, num_iteration=2)
        np.testing.assert_allclose(y_pred, y_pred_new)
    except MemoryError:
2893
        pytest.skipTest("not enough RAM")
2894
2895


2896
2897
2898
@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
2899
def test_get_split_value_histogram():
2900
2901
2902
2903
    X, y = make_synthetic_regression()
    X = np.repeat(X, 3, axis=0)
    y = np.repeat(y, 3, axis=0)
    X[:, 2] = np.random.default_rng(0).integers(0, 20, size=X.shape[0])
2904
    lgb_train = lgb.Dataset(X, y, categorical_feature=[2])
2905
    gbm = lgb.train({"verbose": -1}, lgb_train, num_boost_round=20)
2906
    # test XGBoost-style return value
2907
    params = {"feature": 0, "xgboost_style": True}
2908
2909
    assert gbm.get_split_value_histogram(**params).shape == (12, 2)
    assert gbm.get_split_value_histogram(bins=999, **params).shape == (12, 2)
2910
2911
2912
2913
    assert gbm.get_split_value_histogram(bins=-1, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=0, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=1, **params).shape == (1, 2)
    assert gbm.get_split_value_histogram(bins=2, **params).shape == (2, 2)
2914
2915
    assert gbm.get_split_value_histogram(bins=6, **params).shape == (6, 2)
    assert gbm.get_split_value_histogram(bins=7, **params).shape == (7, 2)
2916
2917
2918
    if lgb.compat.PANDAS_INSTALLED:
        np.testing.assert_allclose(
            gbm.get_split_value_histogram(0, xgboost_style=True).values,
2919
            gbm.get_split_value_histogram(gbm.feature_name()[0], xgboost_style=True).values,
2920
2921
2922
        )
        np.testing.assert_allclose(
            gbm.get_split_value_histogram(X.shape[-1] - 1, xgboost_style=True).values,
2923
            gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1], xgboost_style=True).values,
2924
2925
2926
2927
        )
    else:
        np.testing.assert_allclose(
            gbm.get_split_value_histogram(0, xgboost_style=True),
2928
            gbm.get_split_value_histogram(gbm.feature_name()[0], xgboost_style=True),
2929
2930
2931
        )
        np.testing.assert_allclose(
            gbm.get_split_value_histogram(X.shape[-1] - 1, xgboost_style=True),
2932
            gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1], xgboost_style=True),
2933
2934
2935
        )
    # test numpy-style return value
    hist, bins = gbm.get_split_value_histogram(0)
2936
2937
    assert len(hist) == 20
    assert len(bins) == 21
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
    hist, bins = gbm.get_split_value_histogram(0, bins=999)
    assert len(hist) == 999
    assert len(bins) == 1000
    with pytest.raises(ValueError):
        gbm.get_split_value_histogram(0, bins=-1)
    with pytest.raises(ValueError):
        gbm.get_split_value_histogram(0, bins=0)
    hist, bins = gbm.get_split_value_histogram(0, bins=1)
    assert len(hist) == 1
    assert len(bins) == 2
    hist, bins = gbm.get_split_value_histogram(0, bins=2)
    assert len(hist) == 2
    assert len(bins) == 3
    hist, bins = gbm.get_split_value_histogram(0, bins=6)
    assert len(hist) == 6
    assert len(bins) == 7
    hist, bins = gbm.get_split_value_histogram(0, bins=7)
    assert len(hist) == 7
    assert len(bins) == 8
    hist_idx, bins_idx = gbm.get_split_value_histogram(0)
    hist_name, bins_name = gbm.get_split_value_histogram(gbm.feature_name()[0])
    np.testing.assert_array_equal(hist_idx, hist_name)
    np.testing.assert_allclose(bins_idx, bins_name)
    hist_idx, bins_idx = gbm.get_split_value_histogram(X.shape[-1] - 1)
    hist_name, bins_name = gbm.get_split_value_histogram(gbm.feature_name()[X.shape[-1] - 1])
    np.testing.assert_array_equal(hist_idx, hist_name)
    np.testing.assert_allclose(bins_idx, bins_name)
    # test bins string type
2966
2967
    hist_vals, bin_edges = gbm.get_split_value_histogram(0, bins="auto")
    hist = gbm.get_split_value_histogram(0, bins="auto", xgboost_style=True)
2968
2969
    if lgb.compat.PANDAS_INSTALLED:
        mask = hist_vals > 0
2970
2971
        np.testing.assert_array_equal(hist_vals[mask], hist["Count"].values)
        np.testing.assert_allclose(bin_edges[1:][mask], hist["SplitValue"].values)
2972
2973
2974
2975
    else:
        mask = hist_vals > 0
        np.testing.assert_array_equal(hist_vals[mask], hist[:, 1])
        np.testing.assert_allclose(bin_edges[1:][mask], hist[:, 0])
2976
2977
2978
    # test histogram is disabled for categorical features
    with pytest.raises(lgb.basic.LightGBMError):
        gbm.get_split_value_histogram(2)
2979
2980


2981
2982
2983
@pytest.mark.skipif(
    getenv("TASK", "") == "cuda", reason="Skip due to differences in implementation details of CUDA version"
)
2984
def test_early_stopping_for_only_first_metric():
2985
    def metrics_combination_train_regression(valid_sets, metric_list, assumed_iteration, first_metric_only, feval=None):
2986
        params = {
2987
2988
2989
2990
2991
2992
            "objective": "regression",
            "learning_rate": 1.1,
            "num_leaves": 10,
            "metric": metric_list,
            "verbose": -1,
            "seed": 123,
2993
        }
2994
2995
2996
2997
2998
2999
        gbm = lgb.train(
            params,
            lgb_train,
            num_boost_round=25,
            valid_sets=valid_sets,
            feval=feval,
3000
            callbacks=[lgb.early_stopping(stopping_rounds=5, first_metric_only=first_metric_only)],
3001
        )
3002
        assert assumed_iteration == gbm.best_iteration
3003

3004
3005
3006
    def metrics_combination_cv_regression(
        metric_list, assumed_iteration, first_metric_only, eval_train_metric, feval=None
    ):
3007
        params = {
3008
3009
3010
3011
3012
3013
3014
            "objective": "regression",
            "learning_rate": 0.9,
            "num_leaves": 10,
            "metric": metric_list,
            "verbose": -1,
            "seed": 123,
            "gpu_use_dp": True,
3015
        }
3016
3017
3018
3019
3020
3021
3022
        ret = lgb.cv(
            params,
            train_set=lgb_train,
            num_boost_round=25,
            stratified=False,
            feval=feval,
            callbacks=[lgb.early_stopping(stopping_rounds=5, first_metric_only=first_metric_only)],
3023
            eval_train_metric=eval_train_metric,
3024
        )
3025
3026
        assert assumed_iteration == len(ret[list(ret.keys())[0]])

3027
    X, y = make_synthetic_regression()
3028
3029
3030
3031
3032
3033
3034
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    X_test1, X_test2, y_test1, y_test2 = train_test_split(X_test, y_test, test_size=0.5, random_state=73)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_valid1 = lgb.Dataset(X_test1, y_test1, reference=lgb_train)
    lgb_valid2 = lgb.Dataset(X_test2, y_test2, reference=lgb_train)

    iter_valid1_l1 = 3
3035
3036
    iter_valid1_l2 = 3
    iter_valid2_l1 = 3
3037
    iter_valid2_l2 = 15
3038
    assert len({iter_valid1_l1, iter_valid1_l2, iter_valid2_l1, iter_valid2_l2}) == 2
3039
3040
3041
3042
    iter_min_l1 = min([iter_valid1_l1, iter_valid2_l1])
    iter_min_l2 = min([iter_valid1_l2, iter_valid2_l2])
    iter_min_valid1 = min([iter_valid1_l1, iter_valid1_l2])

3043
3044
    iter_cv_l1 = 15
    iter_cv_l2 = 13
3045
    assert len({iter_cv_l1, iter_cv_l2}) == 2
3046
3047
3048
3049
3050
3051
3052
    iter_cv_min = min([iter_cv_l1, iter_cv_l2])

    # test for lgb.train
    metrics_combination_train_regression(lgb_valid1, [], iter_valid1_l2, False)
    metrics_combination_train_regression(lgb_valid1, [], iter_valid1_l2, True)
    metrics_combination_train_regression(lgb_valid1, None, iter_valid1_l2, False)
    metrics_combination_train_regression(lgb_valid1, None, iter_valid1_l2, True)
3053
3054
3055
3056
3057
3058
    metrics_combination_train_regression(lgb_valid1, "l2", iter_valid1_l2, True)
    metrics_combination_train_regression(lgb_valid1, "l1", iter_valid1_l1, True)
    metrics_combination_train_regression(lgb_valid1, ["l2", "l1"], iter_valid1_l2, True)
    metrics_combination_train_regression(lgb_valid1, ["l1", "l2"], iter_valid1_l1, True)
    metrics_combination_train_regression(lgb_valid1, ["l2", "l1"], iter_min_valid1, False)
    metrics_combination_train_regression(lgb_valid1, ["l1", "l2"], iter_min_valid1, False)
3059
3060

    # test feval for lgb.train
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
    metrics_combination_train_regression(
        lgb_valid1,
        "None",
        1,
        False,
        feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)],
    )
    metrics_combination_train_regression(
        lgb_valid1,
        "None",
        25,
        True,
        feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)],
    )
    metrics_combination_train_regression(
        lgb_valid1,
        "None",
        1,
        True,
        feval=lambda preds, train_data: [constant_metric(preds, train_data), decreasing_metric(preds, train_data)],
    )
3082
3083

    # test with two valid data for lgb.train
3084
3085
3086
3087
    metrics_combination_train_regression([lgb_valid1, lgb_valid2], ["l2", "l1"], iter_min_l2, True)
    metrics_combination_train_regression([lgb_valid2, lgb_valid1], ["l2", "l1"], iter_min_l2, True)
    metrics_combination_train_regression([lgb_valid1, lgb_valid2], ["l1", "l2"], iter_min_l1, True)
    metrics_combination_train_regression([lgb_valid2, lgb_valid1], ["l1", "l2"], iter_min_l1, True)
3088
3089
3090

    # test for lgb.cv
    metrics_combination_cv_regression(None, iter_cv_l2, True, False)
3091
3092
3093
3094
3095
3096
    metrics_combination_cv_regression("l2", iter_cv_l2, True, False)
    metrics_combination_cv_regression("l1", iter_cv_l1, True, False)
    metrics_combination_cv_regression(["l2", "l1"], iter_cv_l2, True, False)
    metrics_combination_cv_regression(["l1", "l2"], iter_cv_l1, True, False)
    metrics_combination_cv_regression(["l2", "l1"], iter_cv_min, False, False)
    metrics_combination_cv_regression(["l1", "l2"], iter_cv_min, False, False)
3097
    metrics_combination_cv_regression(None, iter_cv_l2, True, True)
3098
3099
3100
3101
3102
3103
    metrics_combination_cv_regression("l2", iter_cv_l2, True, True)
    metrics_combination_cv_regression("l1", iter_cv_l1, True, True)
    metrics_combination_cv_regression(["l2", "l1"], iter_cv_l2, True, True)
    metrics_combination_cv_regression(["l1", "l2"], iter_cv_l1, True, True)
    metrics_combination_cv_regression(["l2", "l1"], iter_cv_min, False, True)
    metrics_combination_cv_regression(["l1", "l2"], iter_cv_min, False, True)
3104
3105

    # test feval for lgb.cv
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
    metrics_combination_cv_regression(
        "None",
        1,
        False,
        False,
        feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)],
    )
    metrics_combination_cv_regression(
        "None",
        25,
        True,
        False,
        feval=lambda preds, train_data: [decreasing_metric(preds, train_data), constant_metric(preds, train_data)],
    )
    metrics_combination_cv_regression(
        "None",
        1,
        True,
        False,
        feval=lambda preds, train_data: [constant_metric(preds, train_data), decreasing_metric(preds, train_data)],
    )
3127
3128
3129
3130
3131
3132


def test_node_level_subcol():
    X, y = load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    params = {
3133
3134
3135
3136
3137
        "objective": "binary",
        "metric": "binary_logloss",
        "feature_fraction_bynode": 0.8,
        "feature_fraction": 1.0,
        "verbose": -1,
3138
3139
3140
3141
    }
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    evals_result = {}
3142
    gbm = lgb.train(
3143
        params, lgb_train, num_boost_round=25, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
3144
    )
3145
3146
    ret = log_loss(y_test, gbm.predict(X_test))
    assert ret < 0.14
3147
3148
    assert evals_result["valid_0"]["binary_logloss"][-1] == pytest.approx(ret)
    params["feature_fraction"] = 0.5
3149
3150
3151
3152
3153
    gbm2 = lgb.train(params, lgb_train, num_boost_round=25)
    ret2 = log_loss(y_test, gbm2.predict(X_test))
    assert ret != ret2


3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
def test_forced_split_feature_indices(tmp_path):
    X, y = make_synthetic_regression()
    forced_split = {
        "feature": 0,
        "threshold": 0.5,
        "left": {"feature": X.shape[1], "threshold": 0.5},
    }
    tmp_split_file = tmp_path / "forced_split.json"
    with open(tmp_split_file, "w") as f:
        f.write(json.dumps(forced_split))
    lgb_train = lgb.Dataset(X, y)
3165
    params = {"objective": "regression", "forcedsplits_filename": tmp_split_file}
3166
    with pytest.raises(lgb.basic.LightGBMError, match="Forced splits file includes feature index"):
3167
        lgb.train(params, lgb_train)
3168
3169


3170
def test_forced_bins():
3171
    x = np.empty((100, 2))
3172
3173
3174
    x[:, 0] = np.arange(0, 1, 0.01)
    x[:, 1] = -np.arange(0, 1, 0.01)
    y = np.arange(0, 1, 0.01)
3175
3176
3177
3178
3179
3180
3181
3182
3183
    forcedbins_filename = Path(__file__).absolute().parents[2] / "examples" / "regression" / "forced_bins.json"
    params = {
        "objective": "regression_l1",
        "max_bin": 5,
        "forcedbins_filename": forcedbins_filename,
        "num_leaves": 2,
        "min_data_in_leaf": 1,
        "verbose": -1,
    }
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
    lgb_x = lgb.Dataset(x, label=y)
    est = lgb.train(params, lgb_x, num_boost_round=20)
    new_x = np.zeros((3, x.shape[1]))
    new_x[:, 0] = [0.31, 0.37, 0.41]
    predicted = est.predict(new_x)
    assert len(np.unique(predicted)) == 3
    new_x[:, 0] = [0, 0, 0]
    new_x[:, 1] = [-0.9, -0.6, -0.3]
    predicted = est.predict(new_x)
    assert len(np.unique(predicted)) == 1
3194
    params["forcedbins_filename"] = ""
3195
3196
3197
3198
    lgb_x = lgb.Dataset(x, label=y)
    est = lgb.train(params, lgb_x, num_boost_round=20)
    predicted = est.predict(new_x)
    assert len(np.unique(predicted)) == 3
3199
3200
    params["forcedbins_filename"] = (
        Path(__file__).absolute().parents[2] / "examples" / "regression" / "forced_bins2.json"
3201
    )
3202
    params["max_bin"] = 11
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
    lgb_x = lgb.Dataset(x[:, :1], label=y)
    est = lgb.train(params, lgb_x, num_boost_round=50)
    predicted = est.predict(x[1:, :1])
    _, counts = np.unique(predicted, return_counts=True)
    assert min(counts) >= 9
    assert max(counts) <= 11


def test_binning_same_sign():
    # test that binning works properly for features with only positive or only negative values
3213
    x = np.empty((99, 2))
3214
3215
3216
    x[:, 0] = np.arange(0.01, 1, 0.01)
    x[:, 1] = -np.arange(0.01, 1, 0.01)
    y = np.arange(0.01, 1, 0.01)
3217
3218
3219
3220
3221
3222
3223
3224
    params = {
        "objective": "regression_l1",
        "max_bin": 5,
        "num_leaves": 2,
        "min_data_in_leaf": 1,
        "verbose": -1,
        "seed": 0,
    }
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
    lgb_x = lgb.Dataset(x, label=y)
    est = lgb.train(params, lgb_x, num_boost_round=20)
    new_x = np.zeros((3, 2))
    new_x[:, 0] = [-1, 0, 1]
    predicted = est.predict(new_x)
    assert predicted[0] == pytest.approx(predicted[1])
    assert predicted[1] != pytest.approx(predicted[2])
    new_x = np.zeros((3, 2))
    new_x[:, 1] = [-1, 0, 1]
    predicted = est.predict(new_x)
    assert predicted[0] != pytest.approx(predicted[1])
    assert predicted[1] == pytest.approx(predicted[2])


def test_dataset_update_params():
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
    default_params = {
        "max_bin": 100,
        "max_bin_by_feature": [20, 10],
        "bin_construct_sample_cnt": 10000,
        "min_data_in_bin": 1,
        "use_missing": False,
        "zero_as_missing": False,
        "categorical_feature": [0],
        "feature_pre_filter": True,
        "pre_partition": False,
        "enable_bundle": True,
        "data_random_seed": 0,
        "is_enable_sparse": True,
        "header": True,
        "two_round": True,
        "label_column": 0,
        "weight_column": 0,
        "group_column": 0,
        "ignore_column": 0,
        "min_data_in_leaf": 10,
        "linear_tree": False,
        "precise_float_parser": True,
        "verbose": -1,
    }
    unchangeable_params = {
        "max_bin": 150,
        "max_bin_by_feature": [30, 5],
        "bin_construct_sample_cnt": 5000,
        "min_data_in_bin": 2,
        "use_missing": True,
        "zero_as_missing": True,
        "categorical_feature": [0, 1],
        "feature_pre_filter": False,
        "pre_partition": True,
        "enable_bundle": False,
        "data_random_seed": 1,
        "is_enable_sparse": False,
        "header": False,
        "two_round": False,
        "label_column": 1,
        "weight_column": 1,
        "group_column": 1,
        "ignore_column": 1,
        "forcedbins_filename": "/some/path/forcedbins.json",
        "min_data_in_leaf": 2,
        "linear_tree": True,
        "precise_float_parser": False,
    }
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
    X = np.random.random((100, 2))
    y = np.random.random(100)

    # decreasing without freeing raw data is allowed
    lgb_data = lgb.Dataset(X, y, params=default_params, free_raw_data=False).construct()
    default_params["min_data_in_leaf"] -= 1
    lgb.train(default_params, lgb_data, num_boost_round=3)

    # decreasing before lazy init is allowed
    lgb_data = lgb.Dataset(X, y, params=default_params)
    default_params["min_data_in_leaf"] -= 1
    lgb.train(default_params, lgb_data, num_boost_round=3)

    # increasing is allowed
    default_params["min_data_in_leaf"] += 2
    lgb.train(default_params, lgb_data, num_boost_round=3)

    # decreasing with disabled filter is allowed
    default_params["feature_pre_filter"] = False
    lgb_data = lgb.Dataset(X, y, params=default_params).construct()
    default_params["min_data_in_leaf"] -= 4
    lgb.train(default_params, lgb_data, num_boost_round=3)

    # decreasing with enabled filter is disallowed;
    # also changes of other params are disallowed
    default_params["feature_pre_filter"] = True
    lgb_data = lgb.Dataset(X, y, params=default_params).construct()
    for key, value in unchangeable_params.items():
        new_params = default_params.copy()
        new_params[key] = value
3318
3319
3320
3321
        if key != "forcedbins_filename":
            param_name = key
        else:
            param_name = "forced bins"
3322
3323
3324
3325
3326
        err_msg = (
            "Reducing `min_data_in_leaf` with `feature_pre_filter=true` may cause *"
            if key == "min_data_in_leaf"
            else f"Cannot change {param_name} *"
        )
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
        with np.testing.assert_raises_regex(lgb.basic.LightGBMError, err_msg):
            lgb.train(new_params, lgb_data, num_boost_round=3)


def test_dataset_params_with_reference():
    default_params = {"max_bin": 100}
    X = np.random.random((100, 2))
    y = np.random.random(100)
    X_val = np.random.random((100, 2))
    y_val = np.random.random(100)
    lgb_train = lgb.Dataset(X, y, params=default_params, free_raw_data=False).construct()
    lgb_val = lgb.Dataset(X_val, y_val, reference=lgb_train, free_raw_data=False).construct()
    assert lgb_train.get_params() == default_params
    assert lgb_val.get_params() == default_params
    lgb.train(default_params, lgb_train, valid_sets=[lgb_val])


def test_extra_trees():
    # check extra trees increases regularization
3346
    X, y = make_synthetic_regression()
3347
    lgb_x = lgb.Dataset(X, label=y)
3348
    params = {"objective": "regression", "num_leaves": 32, "verbose": -1, "extra_trees": False, "seed": 0}
3349
3350
3351
    est = lgb.train(params, lgb_x, num_boost_round=10)
    predicted = est.predict(X)
    err = mean_squared_error(y, predicted)
3352
    params["extra_trees"] = True
3353
3354
3355
3356
3357
3358
3359
3360
    est = lgb.train(params, lgb_x, num_boost_round=10)
    predicted_new = est.predict(X)
    err_new = mean_squared_error(y, predicted_new)
    assert err < err_new


def test_path_smoothing():
    # check path smoothing increases regularization
3361
    X, y = make_synthetic_regression()
3362
    lgb_x = lgb.Dataset(X, label=y)
3363
    params = {"objective": "regression", "num_leaves": 32, "verbose": -1, "seed": 0}
3364
3365
3366
    est = lgb.train(params, lgb_x, num_boost_round=10)
    predicted = est.predict(X)
    err = mean_squared_error(y, predicted)
3367
    params["path_smooth"] = 1
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
    est = lgb.train(params, lgb_x, num_boost_round=10)
    predicted_new = est.predict(X)
    err_new = mean_squared_error(y, predicted_new)
    assert err < err_new


def test_trees_to_dataframe():
    pytest.importorskip("pandas")

    def _imptcs_to_numpy(X, impcts_dict):
3378
3379
        cols = [f"Column_{i}" for i in range(X.shape[1])]
        return [impcts_dict.get(col, 0.0) for col in cols]
3380
3381
3382
3383
3384
3385

    X, y = load_breast_cancer(return_X_y=True)
    data = lgb.Dataset(X, label=y)
    num_trees = 10
    bst = lgb.train({"objective": "binary", "verbose": -1}, data, num_trees)
    tree_df = bst.trees_to_dataframe()
3386
    split_dict = tree_df[~tree_df["split_gain"].isnull()].groupby("split_feature").size().to_dict()
3387

3388
    gains_dict = tree_df.groupby("split_feature")["split_gain"].sum().to_dict()
3389
3390
3391

    tree_split = _imptcs_to_numpy(X, split_dict)
    tree_gains = _imptcs_to_numpy(X, gains_dict)
3392
3393
3394
3395
    mod_split = bst.feature_importance("split")
    mod_gains = bst.feature_importance("gain")
    num_trees_from_df = tree_df["tree_index"].nunique()
    obs_counts_from_df = tree_df.loc[tree_df["node_depth"] == 1, "count"].values
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409

    np.testing.assert_equal(tree_split, mod_split)
    np.testing.assert_allclose(tree_gains, mod_gains)
    assert num_trees_from_df == num_trees
    np.testing.assert_equal(obs_counts_from_df, len(y))

    # test edge case with one leaf
    X = np.ones((10, 2))
    y = np.random.rand(10)
    data = lgb.Dataset(X, label=y)
    bst = lgb.train({"objective": "binary", "verbose": -1}, data, num_trees)
    tree_df = bst.trees_to_dataframe()

    assert len(tree_df) == 1
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
    assert tree_df.loc[0, "tree_index"] == 0
    assert tree_df.loc[0, "node_depth"] == 1
    assert tree_df.loc[0, "node_index"] == "0-L0"
    assert tree_df.loc[0, "value"] is not None
    for col in (
        "left_child",
        "right_child",
        "parent_index",
        "split_feature",
        "split_gain",
        "threshold",
        "decision_type",
        "missing_direction",
        "missing_type",
        "weight",
        "count",
    ):
3427
3428
3429
3430
        assert tree_df.loc[0, col] is None


def test_interaction_constraints():
3431
    X, y = make_synthetic_regression(n_samples=200)
3432
3433
3434
    num_features = X.shape[1]
    train_data = lgb.Dataset(X, label=y)
    # check that constraint containing all features is equivalent to no constraint
3435
    params = {"verbose": -1, "seed": 0}
3436
3437
    est = lgb.train(params, train_data, num_boost_round=10)
    pred1 = est.predict(X)
3438
    est = lgb.train(dict(params, interaction_constraints=[list(range(num_features))]), train_data, num_boost_round=10)
3439
3440
3441
    pred2 = est.predict(X)
    np.testing.assert_allclose(pred1, pred2)
    # check that constraint partitioning the features reduces train accuracy
3442
    est = lgb.train(dict(params, interaction_constraints=[[0, 2], [1, 3]]), train_data, num_boost_round=10)
3443
3444
3445
    pred3 = est.predict(X)
    assert mean_squared_error(y, pred1) < mean_squared_error(y, pred3)
    # check that constraints consisting of single features reduce accuracy further
3446
3447
3448
    est = lgb.train(
        dict(params, interaction_constraints=[[i] for i in range(num_features)]), train_data, num_boost_round=10
    )
3449
3450
3451
3452
3453
3454
    pred4 = est.predict(X)
    assert mean_squared_error(y, pred3) < mean_squared_error(y, pred4)
    # test that interaction constraints work when not all features are used
    X = np.concatenate([np.zeros((X.shape[0], 1)), X], axis=1)
    num_features = X.shape[1]
    train_data = lgb.Dataset(X, label=y)
3455
3456
3457
3458
3459
    est = lgb.train(
        dict(params, interaction_constraints=[[0] + list(range(2, num_features)), [1] + list(range(2, num_features))]),
        train_data,
        num_boost_round=10,
    )
3460
3461


3462
3463
3464
3465
3466
3467
3468
def test_linear_trees_num_threads():
    # check that number of threads does not affect result
    np.random.seed(0)
    x = np.arange(0, 1000, 0.1)
    y = 2 * x + np.random.normal(0, 0.1, len(x))
    x = x[:, np.newaxis]
    lgb_train = lgb.Dataset(x, label=y)
3469
    params = {"verbose": -1, "objective": "regression", "seed": 0, "linear_tree": True, "num_threads": 2}
3470
3471
3472
3473
3474
3475
3476
3477
    est = lgb.train(params, lgb_train, num_boost_round=100)
    pred1 = est.predict(x)
    params["num_threads"] = 4
    est = lgb.train(params, lgb_train, num_boost_round=100)
    pred2 = est.predict(x)
    np.testing.assert_allclose(pred1, pred2)


3478
3479
3480
3481
3482
3483
3484
def test_linear_trees(tmp_path):
    # check that setting linear_tree=True fits better than ordinary trees when data has linear relationship
    np.random.seed(0)
    x = np.arange(0, 100, 0.1)
    y = 2 * x + np.random.normal(0, 0.1, len(x))
    x = x[:, np.newaxis]
    lgb_train = lgb.Dataset(x, label=y)
3485
    params = {"verbose": -1, "metric": "mse", "seed": 0, "num_leaves": 2}
3486
3487
3488
3489
    est = lgb.train(params, lgb_train, num_boost_round=10)
    pred1 = est.predict(x)
    lgb_train = lgb.Dataset(x, label=y)
    res = {}
3490
    est = lgb.train(
3491
        dict(params, linear_tree=True),
3492
3493
3494
        lgb_train,
        num_boost_round=10,
        valid_sets=[lgb_train],
3495
3496
        valid_names=["train"],
        callbacks=[lgb.record_evaluation(res)],
3497
    )
3498
    pred2 = est.predict(x)
3499
    assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred2), abs=1e-1)
3500
3501
3502
3503
3504
3505
3506
3507
    assert mean_squared_error(y, pred2) < mean_squared_error(y, pred1)
    # test again with nans in data
    x[:10] = np.nan
    lgb_train = lgb.Dataset(x, label=y)
    est = lgb.train(params, lgb_train, num_boost_round=10)
    pred1 = est.predict(x)
    lgb_train = lgb.Dataset(x, label=y)
    res = {}
3508
    est = lgb.train(
3509
        dict(params, linear_tree=True),
3510
3511
3512
        lgb_train,
        num_boost_round=10,
        valid_sets=[lgb_train],
3513
3514
        valid_names=["train"],
        callbacks=[lgb.record_evaluation(res)],
3515
    )
3516
    pred2 = est.predict(x)
3517
    assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred2), abs=1e-1)
3518
3519
3520
    assert mean_squared_error(y, pred2) < mean_squared_error(y, pred1)
    # test again with bagging
    res = {}
3521
    est = lgb.train(
3522
        dict(params, linear_tree=True, subsample=0.8, bagging_freq=1),
3523
3524
3525
        lgb_train,
        num_boost_round=10,
        valid_sets=[lgb_train],
3526
3527
        valid_names=["train"],
        callbacks=[lgb.record_evaluation(res)],
3528
    )
3529
    pred = est.predict(x)
3530
    assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred), abs=1e-1)
3531
3532
3533
3534
3535
3536
    # test with a feature that has only one non-nan value
    x = np.concatenate([np.ones([x.shape[0], 1]), x], 1)
    x[500:, 1] = np.nan
    y[500:] += 10
    lgb_train = lgb.Dataset(x, label=y)
    res = {}
3537
    est = lgb.train(
3538
        dict(params, linear_tree=True, subsample=0.8, bagging_freq=1),
3539
3540
3541
        lgb_train,
        num_boost_round=10,
        valid_sets=[lgb_train],
3542
3543
        valid_names=["train"],
        callbacks=[lgb.record_evaluation(res)],
3544
    )
3545
    pred = est.predict(x)
3546
    assert res["train"]["l2"][-1] == pytest.approx(mean_squared_error(y, pred), abs=1e-1)
3547
3548
3549
3550
    # test with a categorical feature
    x[:250, 0] = 0
    y[:250] += 10
    lgb_train = lgb.Dataset(x, label=y)
3551
3552
3553
3554
3555
3556
    est = lgb.train(
        dict(params, linear_tree=True, subsample=0.8, bagging_freq=1),
        lgb_train,
        num_boost_round=10,
        categorical_feature=[0],
    )
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
    # test refit: same results on same data
    est2 = est.refit(x, label=y)
    p1 = est.predict(x)
    p2 = est2.predict(x)
    assert np.mean(np.abs(p1 - p2)) < 2

    # test refit with save and load
    temp_model = str(tmp_path / "temp_model.txt")
    est.save_model(temp_model)
    est2 = lgb.Booster(model_file=temp_model)
    est2 = est2.refit(x, label=y)
    p1 = est.predict(x)
    p2 = est2.predict(x)
    assert np.mean(np.abs(p1 - p2)) < 2
    # test refit: different results training on different data
    est3 = est.refit(x[:100, :], label=y[:100])
    p3 = est3.predict(x)
    assert np.mean(np.abs(p2 - p1)) > np.abs(np.max(p3 - p1))
    # test when num_leaves - 1 < num_features and when num_leaves - 1 > num_features
    X_train, _, y_train, _ = train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2)
3577
    params = {"linear_tree": True, "verbose": -1, "metric": "mse", "seed": 0}
3578
3579
3580
3581
3582
3583
    train_data = lgb.Dataset(X_train, label=y_train, params=dict(params, num_leaves=2))
    est = lgb.train(params, train_data, num_boost_round=10, categorical_feature=[0])
    train_data = lgb.Dataset(X_train, label=y_train, params=dict(params, num_leaves=60))
    est = lgb.train(params, train_data, num_boost_round=10, categorical_feature=[0])


3584
def test_save_and_load_linear(tmp_path):
3585
3586
3587
    X_train, X_test, y_train, y_test = train_test_split(
        *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2
    )
3588
    X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], 1)
3589
3590
3591
    X_train[: X_train.shape[0] // 2, 0] = 0
    y_train[: X_train.shape[0] // 2] = 1
    params = {"linear_tree": True}
3592
3593
3594
3595
    train_data_1 = lgb.Dataset(X_train, label=y_train, params=params)
    est_1 = lgb.train(params, train_data_1, num_boost_round=10, categorical_feature=[0])
    pred_1 = est_1.predict(X_train)

3596
    tmp_dataset = str(tmp_path / "temp_dataset.bin")
3597
3598
3599
3600
3601
3602
    train_data_1.save_binary(tmp_dataset)
    train_data_2 = lgb.Dataset(tmp_dataset)
    est_2 = lgb.train(params, train_data_2, num_boost_round=10)
    pred_2 = est_2.predict(X_train)
    np.testing.assert_allclose(pred_1, pred_2)

3603
    model_file = str(tmp_path / "model.txt")
3604
3605
3606
3607
3608
3609
    est_2.save_model(model_file)
    est_3 = lgb.Booster(model_file=model_file)
    pred_3 = est_3.predict(X_train)
    np.testing.assert_allclose(pred_2, pred_3)


3610
3611
3612
def test_linear_single_leaf():
    X_train, y_train = load_breast_cancer(return_X_y=True)
    train_data = lgb.Dataset(X_train, label=y_train)
3613
    params = {"objective": "binary", "linear_tree": True, "min_sum_hessian": 5000}
3614
3615
3616
3617
3618
    bst = lgb.train(params, train_data, num_boost_round=5)
    y_pred = bst.predict(X_train)
    assert log_loss(y_train, y_pred) < 0.661


3619
3620
3621
3622
3623
def test_predict_with_start_iteration():
    def inner_test(X, y, params, early_stopping_rounds):
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
        train_data = lgb.Dataset(X_train, label=y_train)
        valid_data = lgb.Dataset(X_test, label=y_test)
3624
        callbacks = [lgb.early_stopping(early_stopping_rounds)] if early_stopping_rounds is not None else []
3625
        booster = lgb.train(params, train_data, num_boost_round=50, valid_sets=[valid_data], callbacks=callbacks)
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642

        # test that the predict once with all iterations equals summed results with start_iteration and num_iteration
        all_pred = booster.predict(X, raw_score=True)
        all_pred_contrib = booster.predict(X, pred_contrib=True)
        steps = [10, 12]
        for step in steps:
            pred = np.zeros_like(all_pred)
            pred_contrib = np.zeros_like(all_pred_contrib)
            for start_iter in range(0, 50, step):
                pred += booster.predict(X, start_iteration=start_iter, num_iteration=step, raw_score=True)
                pred_contrib += booster.predict(X, start_iteration=start_iter, num_iteration=step, pred_contrib=True)
            np.testing.assert_allclose(all_pred, pred)
            np.testing.assert_allclose(all_pred_contrib, pred_contrib)
        # test the case where start_iteration <= 0, and num_iteration is None
        pred1 = booster.predict(X, start_iteration=-1)
        pred2 = booster.predict(X, num_iteration=booster.best_iteration)
        np.testing.assert_allclose(pred1, pred2)
3643

3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
        # test the case where start_iteration > 0, and num_iteration <= 0
        pred4 = booster.predict(X, start_iteration=10, num_iteration=-1)
        pred5 = booster.predict(X, start_iteration=10, num_iteration=90)
        pred6 = booster.predict(X, start_iteration=10, num_iteration=0)
        np.testing.assert_allclose(pred4, pred5)
        np.testing.assert_allclose(pred4, pred6)

        # test the case where start_iteration > 0, and num_iteration <= 0, with pred_leaf=True
        pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_leaf=True)
        pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_leaf=True)
        pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_leaf=True)
        np.testing.assert_allclose(pred4, pred5)
        np.testing.assert_allclose(pred4, pred6)

        # test the case where start_iteration > 0, and num_iteration <= 0, with pred_contrib=True
        pred4 = booster.predict(X, start_iteration=10, num_iteration=-1, pred_contrib=True)
        pred5 = booster.predict(X, start_iteration=10, num_iteration=40, pred_contrib=True)
        pred6 = booster.predict(X, start_iteration=10, num_iteration=0, pred_contrib=True)
        np.testing.assert_allclose(pred4, pred5)
        np.testing.assert_allclose(pred4, pred6)

    # test for regression
3666
    X, y = make_synthetic_regression()
3667
    params = {"objective": "regression", "verbose": -1, "metric": "l2", "learning_rate": 0.5}
3668
3669
3670
3671
3672
3673
3674
    # test both with and without early stopping
    inner_test(X, y, params, early_stopping_rounds=1)
    inner_test(X, y, params, early_stopping_rounds=5)
    inner_test(X, y, params, early_stopping_rounds=None)

    # test for multi-class
    X, y = load_iris(return_X_y=True)
3675
    params = {"objective": "multiclass", "num_class": 3, "verbose": -1, "metric": "multi_error"}
3676
3677
3678
3679
3680
3681
3682
    # test both with and without early stopping
    inner_test(X, y, params, early_stopping_rounds=1)
    inner_test(X, y, params, early_stopping_rounds=5)
    inner_test(X, y, params, early_stopping_rounds=None)

    # test for binary
    X, y = load_breast_cancer(return_X_y=True)
3683
    params = {"objective": "binary", "verbose": -1, "metric": "auc"}
3684
3685
3686
3687
3688
3689
3690
3691
3692
    # test both with and without early stopping
    inner_test(X, y, params, early_stopping_rounds=1)
    inner_test(X, y, params, early_stopping_rounds=5)
    inner_test(X, y, params, early_stopping_rounds=None)


def test_average_precision_metric():
    # test against sklearn average precision metric
    X, y = load_breast_cancer(return_X_y=True)
3693
    params = {"objective": "binary", "metric": "average_precision", "verbose": -1}
3694
3695
    res = {}
    lgb_X = lgb.Dataset(X, label=y)
3696
3697
    est = lgb.train(params, lgb_X, num_boost_round=10, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(res)])
    ap = res["training"]["average_precision"][-1]
3698
3699
3700
3701
3702
3703
3704
    pred = est.predict(X)
    sklearn_ap = average_precision_score(y, pred)
    assert ap == pytest.approx(sklearn_ap)
    # test that average precision is 1 where model predicts perfectly
    y = y.copy()
    y[:] = 1
    lgb_X = lgb.Dataset(X, label=y)
3705
3706
    lgb.train(params, lgb_X, num_boost_round=1, valid_sets=[lgb_X], callbacks=[lgb.record_evaluation(res)])
    assert res["training"]["average_precision"][-1] == pytest.approx(1)
3707
3708
3709
3710
3711
3712


def test_reset_params_works_with_metric_num_class_and_boosting():
    X, y = load_breast_cancer(return_X_y=True)
    dataset_params = {"max_bin": 150}
    booster_params = {
3713
3714
3715
3716
3717
3718
        "objective": "multiclass",
        "max_depth": 4,
        "bagging_fraction": 0.8,
        "metric": ["multi_logloss", "multi_error"],
        "boosting": "gbdt",
        "num_class": 5,
3719
3720
    }
    dtrain = lgb.Dataset(X, y, params=dataset_params)
3721
    bst = lgb.Booster(params=booster_params, train_set=dtrain)
3722
3723
3724
3725

    expected_params = dict(dataset_params, **booster_params)
    assert bst.params == expected_params

3726
    booster_params["bagging_fraction"] += 0.1
3727
3728
3729
3730
3731
    new_bst = bst.reset_parameter(booster_params)

    expected_params = dict(dataset_params, **booster_params)
    assert bst.params == expected_params
    assert new_bst.params == expected_params
3732
3733
3734
3735
3736


def test_dump_model():
    X, y = load_breast_cancer(return_X_y=True)
    train_data = lgb.Dataset(X, label=y)
3737
    params = {"objective": "binary", "verbose": -1}
3738
3739
3740
3741
3742
3743
3744
    bst = lgb.train(params, train_data, num_boost_round=5)
    dumped_model_str = str(bst.dump_model(5, 0))
    assert "leaf_features" not in dumped_model_str
    assert "leaf_coeff" not in dumped_model_str
    assert "leaf_const" not in dumped_model_str
    assert "leaf_value" in dumped_model_str
    assert "leaf_count" in dumped_model_str
3745
    params["linear_tree"] = True
3746
3747
3748
3749
3750
3751
3752
3753
    train_data = lgb.Dataset(X, label=y)
    bst = lgb.train(params, train_data, num_boost_round=5)
    dumped_model_str = str(bst.dump_model(5, 0))
    assert "leaf_features" in dumped_model_str
    assert "leaf_coeff" in dumped_model_str
    assert "leaf_const" in dumped_model_str
    assert "leaf_value" in dumped_model_str
    assert "leaf_count" in dumped_model_str
3754
3755
3756
3757


def test_dump_model_hook():
    def hook(obj):
3758
3759
3760
        if "leaf_value" in obj:
            obj["LV"] = obj["leaf_value"]
            del obj["leaf_value"]
3761
3762
3763
3764
        return obj

    X, y = load_breast_cancer(return_X_y=True)
    train_data = lgb.Dataset(X, label=y)
3765
    params = {"objective": "binary", "verbose": -1}
3766
3767
3768
3769
    bst = lgb.train(params, train_data, num_boost_round=5)
    dumped_model_str = str(bst.dump_model(5, 0, object_hook=hook))
    assert "leaf_value" not in dumped_model_str
    assert "LV" in dumped_model_str
3770
3771


3772
@pytest.mark.skipif(getenv("TASK", "") == "cuda", reason="Forced splits are not yet supported by CUDA version")
3773
def test_force_split_with_feature_fraction(tmp_path):
3774
    X, y = make_synthetic_regression()
3775
3776
3777
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)

3778
    forced_split = {"feature": 0, "threshold": 0.5, "right": {"feature": 2, "threshold": 10.0}}
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788

    tmp_split_file = tmp_path / "forced_split.json"
    with open(tmp_split_file, "w") as f:
        f.write(json.dumps(forced_split))

    params = {
        "objective": "regression",
        "feature_fraction": 0.6,
        "force_col_wise": True,
        "feature_fraction_seed": 1,
3789
        "forcedsplits_filename": tmp_split_file,
3790
3791
3792
3793
    }

    gbm = lgb.train(params, lgb_train)
    ret = mean_absolute_error(y_test, gbm.predict(X_test))
3794
    assert ret < 15.7
3795
3796
3797
3798
3799

    tree_info = gbm.dump_model()["tree_info"]
    assert len(tree_info) > 1
    for tree in tree_info:
        tree_structure = tree["tree_structure"]
3800
        assert tree_structure["split_feature"] == 0
3801
3802


3803
3804
3805
3806
3807
3808
def test_goss_boosting_and_strategy_equivalent():
    X, y = make_synthetic_regression(n_samples=10_000, n_features=10, n_informative=5, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    base_params = {
3809
3810
3811
3812
3813
3814
3815
        "metric": "l2",
        "verbose": -1,
        "bagging_seed": 0,
        "learning_rate": 0.05,
        "num_threads": 1,
        "force_row_wise": True,
        "gpu_use_dp": True,
3816
    }
3817
    params1 = {**base_params, "boosting": "goss"}
3818
    evals_result1 = {}
3819
3820
3821
3822
    lgb.train(
        params1, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result1)]
    )
    params2 = {**base_params, "data_sample_strategy": "goss"}
3823
    evals_result2 = {}
3824
3825
3826
3827
    lgb.train(
        params2, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result2)]
    )
    assert evals_result1["valid_0"]["l2"] == evals_result2["valid_0"]["l2"]
3828
3829
3830
3831
3832
3833
3834
3835
3836


def test_sample_strategy_with_boosting():
    X, y = make_synthetic_regression(n_samples=10_000, n_features=10, n_informative=5, random_state=42)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
    lgb_train = lgb.Dataset(X_train, y_train)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

    base_params = {
3837
3838
3839
3840
3841
        "metric": "l2",
        "verbose": -1,
        "num_threads": 1,
        "force_row_wise": True,
        "gpu_use_dp": True,
3842
3843
    }

3844
    params1 = {**base_params, "boosting": "dart", "data_sample_strategy": "goss"}
3845
    evals_result = {}
3846
3847
3848
3849
    gbm = lgb.train(
        params1, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res1 = evals_result["valid_0"]["l2"][-1]
3850
3851
3852
3853
    test_res1 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res1 == pytest.approx(3149.393862, abs=1.0)
    assert eval_res1 == pytest.approx(test_res1)

3854
    params2 = {**base_params, "boosting": "gbdt", "data_sample_strategy": "goss"}
3855
    evals_result = {}
3856
3857
3858
3859
    gbm = lgb.train(
        params2, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res2 = evals_result["valid_0"]["l2"][-1]
3860
3861
3862
3863
    test_res2 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res2 == pytest.approx(2547.715968, abs=1.0)
    assert eval_res2 == pytest.approx(test_res2)

3864
    params3 = {**base_params, "boosting": "goss", "data_sample_strategy": "goss"}
3865
    evals_result = {}
3866
3867
3868
3869
    gbm = lgb.train(
        params3, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res3 = evals_result["valid_0"]["l2"][-1]
3870
3871
3872
3873
    test_res3 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res3 == pytest.approx(2547.715968, abs=1.0)
    assert eval_res3 == pytest.approx(test_res3)

3874
    params4 = {**base_params, "boosting": "rf", "data_sample_strategy": "goss"}
3875
    evals_result = {}
3876
3877
3878
3879
    gbm = lgb.train(
        params4, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res4 = evals_result["valid_0"]["l2"][-1]
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
    test_res4 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res4 == pytest.approx(2095.538735, abs=1.0)
    assert eval_res4 == pytest.approx(test_res4)

    assert test_res1 != test_res2
    assert eval_res1 != eval_res2
    assert test_res2 == test_res3
    assert eval_res2 == eval_res3
    assert eval_res1 != eval_res4
    assert test_res1 != test_res4
    assert eval_res2 != eval_res4
    assert test_res2 != test_res4

3893
3894
3895
3896
3897
3898
3899
    params5 = {
        **base_params,
        "boosting": "dart",
        "data_sample_strategy": "bagging",
        "bagging_freq": 1,
        "bagging_fraction": 0.5,
    }
3900
    evals_result = {}
3901
3902
3903
3904
    gbm = lgb.train(
        params5, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res5 = evals_result["valid_0"]["l2"][-1]
3905
3906
3907
3908
    test_res5 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res5 == pytest.approx(3134.866931, abs=1.0)
    assert eval_res5 == pytest.approx(test_res5)

3909
3910
3911
3912
3913
3914
3915
    params6 = {
        **base_params,
        "boosting": "gbdt",
        "data_sample_strategy": "bagging",
        "bagging_freq": 1,
        "bagging_fraction": 0.5,
    }
3916
    evals_result = {}
3917
3918
3919
3920
    gbm = lgb.train(
        params6, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res6 = evals_result["valid_0"]["l2"][-1]
3921
3922
3923
3924
3925
3926
    test_res6 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res6 == pytest.approx(2539.792378, abs=1.0)
    assert eval_res6 == pytest.approx(test_res6)
    assert test_res5 != test_res6
    assert eval_res5 != eval_res6

3927
3928
3929
3930
3931
3932
3933
    params7 = {
        **base_params,
        "boosting": "rf",
        "data_sample_strategy": "bagging",
        "bagging_freq": 1,
        "bagging_fraction": 0.5,
    }
3934
    evals_result = {}
3935
3936
3937
3938
    gbm = lgb.train(
        params7, lgb_train, num_boost_round=10, valid_sets=lgb_eval, callbacks=[lgb.record_evaluation(evals_result)]
    )
    eval_res7 = evals_result["valid_0"]["l2"][-1]
3939
3940
3941
3942
3943
3944
3945
3946
3947
    test_res7 = mean_squared_error(y_test, gbm.predict(X_test))
    assert test_res7 == pytest.approx(1518.704481, abs=1.0)
    assert eval_res7 == pytest.approx(test_res7)
    assert test_res5 != test_res7
    assert eval_res5 != eval_res7
    assert test_res6 != test_res7
    assert eval_res6 != eval_res7


3948
3949
3950
3951
3952
def test_record_evaluation_with_train():
    X, y = make_synthetic_regression()
    ds = lgb.Dataset(X, y)
    eval_result = {}
    callbacks = [lgb.record_evaluation(eval_result)]
3953
    params = {"objective": "l2", "num_leaves": 3}
3954
3955
    num_boost_round = 5
    bst = lgb.train(params, ds, num_boost_round=num_boost_round, valid_sets=[ds], callbacks=callbacks)
3956
    assert list(eval_result.keys()) == ["training"]
3957
3958
3959
3960
3961
    train_mses = []
    for i in range(num_boost_round):
        pred = bst.predict(X, num_iteration=i + 1)
        mse = mean_squared_error(y, pred)
        train_mses.append(mse)
3962
    np.testing.assert_allclose(eval_result["training"]["l2"], train_mses)
3963
3964


3965
@pytest.mark.parametrize("train_metric", [False, True])
3966
3967
3968
3969
3970
def test_record_evaluation_with_cv(train_metric):
    X, y = make_synthetic_regression()
    ds = lgb.Dataset(X, y)
    eval_result = {}
    callbacks = [lgb.record_evaluation(eval_result)]
3971
3972
3973
3974
3975
3976
    metrics = ["l2", "rmse"]
    params = {"objective": "l2", "num_leaves": 3, "metric": metrics}
    cv_hist = lgb.cv(
        params, ds, num_boost_round=5, stratified=False, callbacks=callbacks, eval_train_metric=train_metric
    )
    expected_datasets = {"valid"}
3977
    if train_metric:
3978
        expected_datasets.add("train")
3979
3980
3981
    assert set(eval_result.keys()) == expected_datasets
    for dataset in expected_datasets:
        for metric in metrics:
3982
3983
3984
            for agg in ("mean", "stdv"):
                key = f"{dataset} {metric}-{agg}"
                np.testing.assert_allclose(cv_hist[key], eval_result[dataset][f"{metric}-{agg}"])
3985
3986
3987


def test_pandas_with_numpy_regular_dtypes():
3988
3989
3990
3991
    pd = pytest.importorskip("pandas")
    uints = ["uint8", "uint16", "uint32", "uint64"]
    ints = ["int8", "int16", "int32", "int64"]
    bool_and_floats = ["bool", "float16", "float32", "float64"]
3992
3993
3994
3995
    rng = np.random.RandomState(42)

    n_samples = 100
    # data as float64
3996
3997
3998
3999
4000
4001
4002
4003
    df = pd.DataFrame(
        {
            "x1": rng.randint(0, 2, n_samples),
            "x2": rng.randint(1, 3, n_samples),
            "x3": 10 * rng.randint(1, 3, n_samples),
            "x4": 100 * rng.randint(1, 3, n_samples),
        }
    )
4004
    df = df.astype(np.float64)
4005
    y = df["x1"] * (df["x2"] + df["x3"] + df["x4"])
4006
    ds = lgb.Dataset(df, y)
4007
    params = {"objective": "l2", "num_leaves": 31, "min_child_samples": 1}
4008
4009
4010
4011
    bst = lgb.train(params, ds, num_boost_round=5)
    preds = bst.predict(df)

    # test all features were used
4012
    assert bst.trees_to_dataframe()["split_feature"].nunique() == df.shape[1]
4013
4014
4015
4016
4017
4018
    # test the score is better than predicting the mean
    baseline = np.full_like(y, y.mean())
    assert mean_squared_error(y, preds) < mean_squared_error(y, baseline)

    # test all predictions are equal using different input dtypes
    for target_dtypes in [uints, ints, bool_and_floats]:
4019
        df2 = df.astype({f"x{i}": dtype for i, dtype in enumerate(target_dtypes, start=1)})
4020
4021
4022
4023
4024
4025
4026
4027
        assert df2.dtypes.tolist() == target_dtypes
        ds2 = lgb.Dataset(df2, y)
        bst2 = lgb.train(params, ds2, num_boost_round=5)
        preds2 = bst2.predict(df2)
        np.testing.assert_allclose(preds, preds2)


def test_pandas_nullable_dtypes():
4028
    pd = pytest.importorskip("pandas")
4029
    rng = np.random.RandomState(0)
4030
4031
4032
4033
4034
4035
4036
4037
    df = pd.DataFrame(
        {
            "x1": rng.randint(1, 3, size=100),
            "x2": np.linspace(-1, 1, 100),
            "x3": pd.arrays.SparseArray(rng.randint(0, 11, size=100)),
            "x4": rng.rand(100) < 0.5,
        }
    )
4038
    # introduce some missing values
4039
4040
4041
    df.loc[1, "x1"] = np.nan
    df.loc[2, "x2"] = np.nan
    df.loc[3, "x4"] = np.nan
4042
    # the previous line turns x3 into object dtype in recent versions of pandas
4043
4044
    df["x4"] = df["x4"].astype(np.float64)
    y = df["x1"] * df["x2"] + df["x3"] * (1 + df["x4"])
4045
4046
4047
    y = y.fillna(0)

    # train with regular dtypes
4048
    params = {"objective": "l2", "num_leaves": 31, "min_child_samples": 1}
4049
4050
4051
4052
4053
4054
    ds = lgb.Dataset(df, y)
    bst = lgb.train(params, ds, num_boost_round=5)
    preds = bst.predict(df)

    # convert to nullable dtypes
    df2 = df.copy()
4055
4056
4057
    df2["x1"] = df2["x1"].astype("Int32")
    df2["x2"] = df2["x2"].astype("Float64")
    df2["x4"] = df2["x4"].astype("boolean")
4058
4059
4060
4061
4062
4063
4064
4065

    # test training succeeds
    ds_nullable_dtypes = lgb.Dataset(df2, y)
    bst_nullable_dtypes = lgb.train(params, ds_nullable_dtypes, num_boost_round=5)
    preds_nullable_dtypes = bst_nullable_dtypes.predict(df2)

    trees_df = bst_nullable_dtypes.trees_to_dataframe()
    # test all features were used
4066
    assert trees_df["split_feature"].nunique() == df.shape[1]
4067
4068
4069
4070
4071
4072
    # test the score is better than predicting the mean
    baseline = np.full_like(y, y.mean())
    assert mean_squared_error(y, preds) < mean_squared_error(y, baseline)

    # test equal predictions
    np.testing.assert_allclose(preds, preds_nullable_dtypes)
4073
4074
4075
4076
4077


def test_boost_from_average_with_single_leaf_trees():
    # test data are taken from bug report
    # https://github.com/microsoft/LightGBM/issues/4708
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
    X = np.array(
        [
            [1021.0589, 1018.9578],
            [1023.85754, 1018.7854],
            [1024.5468, 1018.88513],
            [1019.02954, 1018.88513],
            [1016.79926, 1018.88513],
            [1007.6, 1018.88513],
        ],
        dtype=np.float32,
    )
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
    y = np.array([1023.8, 1024.6, 1024.4, 1023.8, 1022.0, 1014.4], dtype=np.float32)
    params = {
        "extra_trees": True,
        "min_data_in_bin": 1,
        "extra_seed": 7,
        "objective": "regression",
        "verbose": -1,
        "boost_from_average": True,
        "min_data_in_leaf": 1,
    }
    train_set = lgb.Dataset(X, y)
    model = lgb.train(params=params, train_set=train_set, num_boost_round=10)

    preds = model.predict(X)
    mean_preds = np.mean(preds)
    assert y.min() <= mean_preds <= y.max()
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130


def test_cegb_split_buffer_clean():
    # modified from https://github.com/microsoft/LightGBM/issues/3679#issuecomment-938652811
    # and https://github.com/microsoft/LightGBM/pull/5087
    # test that the ``splits_per_leaf_`` of CEGB is cleaned before training a new tree
    # which is done in the fix #5164
    # without the fix:
    #    Check failed: (best_split_info.left_count) > (0)

    R, C = 1000, 100
    seed = 29
    np.random.seed(seed)
    data = np.random.randn(R, C)
    for i in range(1, C):
        data[i] += data[0] * np.random.randn()

    N = int(0.8 * len(data))
    train_data = data[:N]
    test_data = data[N:]
    train_y = np.sum(train_data, axis=1)
    test_y = np.sum(test_data, axis=1)

    train = lgb.Dataset(train_data, train_y, free_raw_data=True)

    params = {
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
        "boosting_type": "gbdt",
        "objective": "regression",
        "max_bin": 255,
        "num_leaves": 31,
        "seed": 0,
        "learning_rate": 0.1,
        "min_data_in_leaf": 0,
        "verbose": -1,
        "min_split_gain": 1000.0,
        "cegb_penalty_feature_coupled": 5 * np.arange(C),
        "cegb_penalty_split": 0.0002,
        "cegb_tradeoff": 10.0,
        "force_col_wise": True,
4144
4145
4146
4147
4148
4149
    }

    model = lgb.train(params, train, num_boost_round=10)
    predicts = model.predict(test_data)
    rmse = np.sqrt(mean_squared_error(test_y, predicts))
    assert rmse < 10.0
4150
4151


4152
4153
4154
4155
def test_verbosity_and_verbose(capsys):
    X, y = make_synthetic_regression()
    ds = lgb.Dataset(X, y)
    params = {
4156
4157
4158
        "num_leaves": 3,
        "verbose": 1,
        "verbosity": 0,
4159
4160
    }
    lgb.train(params, ds, num_boost_round=1)
4161
    expected_msg = "[LightGBM] [Warning] verbosity is set=0, verbose=1 will be ignored. " "Current value: verbosity=0"
4162
4163
4164
4165
    stdout = capsys.readouterr().out
    assert expected_msg in stdout


4166
4167
@pytest.mark.parametrize("verbosity_param", lgb.basic._ConfigAliases.get("verbosity"))
@pytest.mark.parametrize("verbosity", [-1, 0])
4168
4169
4170
4171
def test_verbosity_can_suppress_alias_warnings(capsys, verbosity_param, verbosity):
    X, y = make_synthetic_regression()
    ds = lgb.Dataset(X, y)
    params = {
4172
4173
4174
4175
        "num_leaves": 3,
        "subsample": 0.75,
        "bagging_fraction": 0.8,
        "force_col_wise": True,
4176
4177
4178
4179
        verbosity_param: verbosity,
    }
    lgb.train(params, ds, num_boost_round=1)
    expected_msg = (
4180
4181
        "[LightGBM] [Warning] bagging_fraction is set=0.8, subsample=0.75 will be ignored. "
        "Current value: bagging_fraction=0.8"
4182
4183
4184
4185
4186
    )
    stdout = capsys.readouterr().out
    if verbosity >= 0:
        assert expected_msg in stdout
    else:
4187
        assert re.search(r"\[LightGBM\]", stdout) is None
4188
4189


4190
@pytest.mark.skipif(not PANDAS_INSTALLED, reason="pandas is not installed")
4191
4192
def test_validate_features():
    X, y = make_synthetic_regression()
4193
    features = ["x1", "x2", "x3", "x4"]
4194
4195
    df = pd_DataFrame(X, columns=features)
    ds = lgb.Dataset(df, y)
4196
    bst = lgb.train({"num_leaves": 15, "verbose": -1}, ds, num_boost_round=10)
4197
4198
4199
    assert bst.feature_name() == features

    # try to predict with a different feature
4200
    df2 = df.rename(columns={"x3": "z"})
4201
4202
4203
4204
4205
    with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x3' at position 2 but found 'z'"):
        bst.predict(df2, validate_features=True)

    # check that disabling the check doesn't raise the error
    bst.predict(df2, validate_features=False)
4206
4207
4208
4209
4210
4211
4212

    # try to refit with a different feature
    with pytest.raises(lgb.basic.LightGBMError, match="Expected 'x3' at position 2 but found 'z'"):
        bst.refit(df2, y, validate_features=True)

    # check that disabling the check doesn't raise the error
    bst.refit(df2, y, validate_features=False)
4213
4214


4215
4216
4217
4218
4219
4220
4221
def test_train_and_cv_raise_informative_error_for_train_set_of_wrong_type():
    with pytest.raises(TypeError, match=r"train\(\) only accepts Dataset object, train_set has type 'list'\."):
        lgb.train({}, train_set=[])
    with pytest.raises(TypeError, match=r"cv\(\) only accepts Dataset object, train_set has type 'list'\."):
        lgb.cv({}, train_set=[])


4222
@pytest.mark.parametrize("num_boost_round", [-7, -1, 0])
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
def test_train_and_cv_raise_informative_error_for_impossible_num_boost_round(num_boost_round):
    X, y = make_synthetic_regression(n_samples=100)
    error_msg = rf"num_boost_round must be greater than 0\. Got {num_boost_round}\."
    with pytest.raises(ValueError, match=error_msg):
        lgb.train({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round)
    with pytest.raises(ValueError, match=error_msg):
        lgb.cv({}, train_set=lgb.Dataset(X, y), num_boost_round=num_boost_round)


def test_train_raises_informative_error_if_any_valid_sets_are_not_dataset_objects():
    X, y = make_synthetic_regression(n_samples=100)
    X_valid = X * 2.0
4235
4236
4237
    with pytest.raises(
        TypeError, match=r"Every item in valid_sets must be a Dataset object\. Item 1 has type 'tuple'\."
    ):
4238
4239
4240
        lgb.train(
            params={},
            train_set=lgb.Dataset(X, y),
4241
            valid_sets=[lgb.Dataset(X_valid, y), ([1.0], [2.0]), [5.6, 5.7, 5.8]],
4242
4243
4244
        )


4245
4246
def test_train_raises_informative_error_for_params_of_wrong_type():
    X, y = make_synthetic_regression()
4247
    params = {"num_leaves": "too-many"}
4248
    dtrain = lgb.Dataset(X, label=y)
4249
    with pytest.raises(lgb.basic.LightGBMError, match='Parameter num_leaves should be of type int, got "too-many"'):
4250
        lgb.train(params, dtrain)
4251
4252
4253
4254
4255


def test_quantized_training():
    X, y = make_synthetic_regression()
    ds = lgb.Dataset(X, label=y)
4256
    bst_params = {"num_leaves": 15, "verbose": -1, "seed": 0}
4257
4258
    bst = lgb.train(bst_params, ds, num_boost_round=10)
    rmse = np.sqrt(np.mean((bst.predict(X) - y) ** 2))
4259
4260
4261
4262
4263
4264
4265
    bst_params.update(
        {
            "use_quantized_grad": True,
            "num_grad_quant_bins": 30,
            "quant_train_renew_leaf": True,
        }
    )
4266
4267
4268
    quant_bst = lgb.train(bst_params, ds, num_boost_round=10)
    quant_rmse = np.sqrt(np.mean((quant_bst.predict(X) - y) ** 2))
    assert quant_rmse < rmse + 6.0